Backtesting & Data Mining

In this article we'll take a look at two related practices that are widely used by traders called Backtesting and Data Mining. These are techniques that are powerful and valuable if we use them correctly, however traders often misuse them. Therefore, we'll also explore two common pitfalls of these techniques, known as the multiple hypothesis problem and overfitting and how to overcome these pitfalls.

Backtesting

Backtesting is just the process of using historical data to test the performance of some trading strategy. Backtesting generally starts with a strategy that we would like to test, for instance buying GBP/USD when it crosses above the 20-day moving average and selling when it crosses below that average. Now we could test that strategy by watching what the market does going forward, but that would take a long time. This is why we use historical data that is already available.

"But wait, wait!" I hear you say. "Couldn't you cheat or at least be biased because you already know what happened in the past?" That's definitely a concern, so a valid backtest will be one in which we aren't familiar with the historical data. We can accomplish this by choosing random time periods or by choosing many different time periods in which to conduct the test.

Now I can hear another group of you saying, "But all that historical data just sitting there waiting to be analyzed is tempting isn't it? Maybe there are profound secrets in that data just waiting for geeks like us to discover it. Would it be so wrong for us to examine that historical data first, to analyze it and see if we can find patterns hidden within it?" This argument is also valid, but it leads us into an area fraught with danger...the world of Data Mining

Data Mining

Data Mining involves searching through data in order to locate patterns and find possible correlations between variables. In the example above involving the 20-day moving average strategy, we just came up with that particular indicator out of the blue, but suppose we had no idea what type of strategy we wanted to test? That's when data mining comes in handy. We could search through our historical data on GBP/USD to see how the price behaved after it crossed many different moving averages. We could check price movements against many other types of indicators as well and see which ones correspond to large price movements.

The subject of data mining can be controversial because as I discussed above it seems a bit like cheating or "looking ahead" in the data. Is data mining a valid scientific technique? On the one hand the scientific method says that we're supposed to make a hypothesis first and then test it against our data, but on the other hand it seems appropriate to do some "exploration" of the data first in order to suggest a hypothesis. So which is right? We can look at the steps in the Scientific Method for a clue to the source of the confusion. The process in general looks like this:

Observation (data) >>> Hypothesis >>> Prediction >>> Experiment (data)

Notice that we can deal with data during both the Observation and Experiment stages. So both views are right. We must use data in order to create a sensible hypothesis, but we also test that hypothesis using data. The trick is simply to make sure that the two sets of data are not the same! We must never test our hypothesis using the same set of data that we used to suggest our hypothesis. In other words, if you use data mining in order to come up with strategy ideas, make sure you use a different set of data to backtest those ideas.

Now we'll turn our attention to the main pitfalls of using data mining and backtesting incorrectly. The general problem is known as "over-optimization" and I prefer to break that problem down into two distinct types. These are the multiple hypothesis problem and overfitting. In a sense they are opposite ways of making the same error. The multiple hypothesis problem involves choosing many simple hypotheses while overfitting involves the creation of one very complex hypothesis.

The Multiple Hypothesis Problem

To see how this problem arises, let's go back to our example where we backtested the 20-day moving average strategy. Let's suppose that we backtest the strategy against ten years of historical market data and lo and behold guess what? The results are not very encouraging. However, being rough and tumble traders as we are, we decide not to give up so easily. What about a ten day moving average? That might work out a little better, so let's backtest it! We run another backtest and we find that the results still aren't stellar, but they're a bit better than the 20-day results. We decide to explore a little and run similar tests with 5-day and 30-day moving averages. Finally it occurs to us that we could actually just test every single moving average up to some point and see how they all perform. So we test the 2-day, 3-day, 4-day, and so on, all the way up to the 50-day moving average.

Now certainly some of these averages will perform poorly and others will perform fairly well, but there will have to be one of them which is the absolute best. For instance we may find that the 32-day moving average turned out to be the best performer during this particular ten year period. Does this mean that there is something special about the 32-day average and that we should be confident that it will perform well in the future? Unfortunately many traders assume this to be the case, and they just stop their analysis at this point, thinking that they've discovered something profound. They have fallen into the "Multiple Hypothesis Problem" pitfall.

The problem is that there is nothing at all unusual or significant about the fact that some average turned out to be the best. After all, we tested almost fifty of them against the same data, so we'd expect to find a few good performers, just by chance. It doesn't mean there's anything special about the particular moving average that "won" in this case. The problem arises because we tested multiple hypotheses until we found one that worked, instead of choosing a single hypothesis and testing it.

Here's a good classic analogy. We could come up with a single hypothesis such as "Scott is great at flipping heads on a coin." From that, we could create a prediction that says, "If the hypothesis is true, Scott will be able to flip 10 heads in a row." Then we can perform a simple experiment to test that hypothesis. If I can flip 10 heads in a row it actually doesn't prove the hypothesis. However if I can't accomplish this feat it definitely disproves the hypothesis. As we do repeated experiments which fail to disprove the hypothesis, then our confidence in its truth grows.

That's the right way to do it. However, what if we had come up with 1,000 hypotheses instead of just the one about me being a good coin flipper? We could make the same hypothesis about 1,000 different people...me, Ed, Cindy, Bill, Sam, etc. Ok, now let's test our multiple hypotheses. We ask all 1000 people to flip a coin. There will probably be about 500 who flip heads. Everyone else can go home. Now we ask those 500 people to flip again, and this time about 250 will flip heads. On the third flip about 125 people flip heads, on the fourth about 63 people are left, and on the fifth flip there are about 32. These 32 people are all pretty amazing aren't they? They've all flipped five heads in a row! If we flip five more times and eliminate half the people each time on average, we will end up with 16, then 8, then 4, then 2 and finally one person left who has flipped ten heads in a row. It's Bill! Bill is a "fantabulous" flipper of coins! Or is he?

Well we really don't know, and that's the point. Bill may have won our contest out of pure chance, or he may very well be the best flipper of heads this side of the Andromeda galaxy. By the same token, we don't know if the 32-day moving average from our example above just performed well in our test by pure chance, or if there is really something special about it. But all we've done so far is to find a hypothesis, namely that the 32-day moving average strategy is profitable (or that Bill is a great coin flipper). We haven't actually tested that hypothesis yet.

So now that we understand that we haven't really discovered anything significant yet about the 32-day moving average or about Bill's ability to flip coins, the natural question to ask is what should we do next? As I mentioned above, many traders never realize that there is a next step required at all. Well, in the case of Bill you'd probably ask, "Aha, but can he flip ten heads in a row again?" In the case of the 32-day moving average, we'd want to test it again, but certainly not against the same data sample that we used to choose that hypothesis. We would choose another ten-year period and see if the strategy worked just as well. We could continue to do this experiment as many times as we wanted until our supply of new ten-year periods ran out. We refer to this as "out of sample testing", and it's the way to avoid this pitfall. There are various methods of such testing, one of which is "cross validation", but we won't get into that much detail here.


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Data Extraction - A Guideline to Use Scrapping Tools Effectively

So many people around the world do not have much knowledge about these scrapping tools. In their views, mining means extracting resources from the earth. In these internet technology days, the new mined resource is data. There are so many data mining software tools are available in the internet to extract specific data from the web. Every company in the world has been dealing with tons of data, managing and converting this data into a useful form is a real hectic work for them. If this right information is not available at the right time a company will lose valuable time to making strategic decisions on this accurate information.

This type of situation will break opportunities in the present competitive market. However, in these situations, the data extraction and data mining tools will help you to take the strategic decisions in right time to reach your goals in this competitive business. There are so many advantages with these tools that you can store customer information in a sequential manner, you can know the operations of your competitors, and also you can figure out your company performance. And it is a critical job to every company to have this information at fingertips when they need this information.

To survive in this competitive business world, this data extraction and data mining are critical in operations of the company. There is a powerful tool called Website scraper used in online digital mining. With this toll, you can filter the data in internet and retrieves the information for specific needs. This scrapping tool is used in various fields and types are numerous. Research, surveillance, and the harvesting of direct marketing leads is just a few ways the website scraper assists professionals in the workplace.

Screen scrapping tool is another tool which useful to extract the data from the web. This is much helpful when you work on the internet to mine data to your local hard disks. It provides a graphical interface allowing you to designate Universal Resource Locator, data elements to be extracted, and scripting logic to traverse pages and work with mined data. You can use this tool as periodical intervals. By using this tool, you can download the database in internet to you spread sheets. The important one in scrapping tools is Data mining software, it will extract the large amount of information from the web, and it will compare that date into a useful format. This tool is used in various sectors of business, especially, for those who are creating leads, budget establishing seeing the competitors charges and analysis the trends in online. With this tool, the information is gathered and immediately uses for your business needs.

Another best scrapping tool is e mailing scrapping tool, this tool crawls the public email addresses from various web sites. You can easily from a large mailing list with this tool. You can use these mailing lists to promote your product through online and proposals sending an offer for related business and many more to do. With this toll, you can find the targeted customers towards your product or potential business parents. This will allows you to expand your business in the online market.

There are so many well established and esteemed organizations are providing these features free of cost as the trial offer to customers. If you want permanent services, you need to pay nominal fees. You can download these services from their valuable web sites also.



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Outsource Data Entry

Need to outsource data entry? It may seem like an easy task, but most businesses outsource data entry tasks. As the number of companies and firms grow, more and more job opportunities for such service providers are opening up. The first step to outsource these tasks is to identify the scope of the job that you want to outsource. This means figuring out exactly what you want the person you will be hiring to do for you. The service provider can help too and will ask if anything is unclear.

The job could be for the finance department which would deal with handling expenses and incomes, or it could be for the Human Resource Department handling the data base of employees and keeping it updates according to new additions or people who leave. Once, the scope of the job has been finalized, the data needs to be assembled. This could be in the form of sheets of paper that contain bills, details of expenses and various other types of tabulated data, both online and offline.

Since the work is so varied and vast, most businesses choose to outsource data entry projects. This can be done by hiring a company, BPO's or by hiring an individual to do the work for them. BPO's have entire teams that specialize in different forms of such tasks and are fluent in working with the many software out there available. There are different advantages to using companies or individuals to outsource data entry work. While individuals have the benefit of being a little bit more flexible when it comes to demands and specifications, companies that work towards performing entry tasks are more efficient and better time managers due to the vast number of people they hire and train to do the job.

When deciding to outsource the data entry task, care must be taken to ensure that the business you are contacting is legitimate and has the means or manpower necessary to perform the task, particularly if you have special software in place just for entering and organizing data. No matter what kind of data you are handing over, be it in the form of sheets of pages that need to be converted into tables, photographs of papers or maybe an audio transcription needs to be done, companies that deal with data entry tasks are capable of handling all kinds of data and converting and tabulating them into any form you want.



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Data Entry Services Widen the Reach of Your Products Globally

Are you into an online business and wish to display your products online. Data Entry Services can serve this purpose for you. Get your products displayed online with these professional Data Entry Services. They can serve you with number of services like updating the descriptions of the products, managing the products online and will look after for your website. Catalogs help you to display the products and bring in direct sales.

A useful online product catalog will help you to categorize your products accordingly and provides your visitors with appropriate and exact product descriptions. The images related to your products help you to maximize the efficiency of your e-commerce store.

Professional Data Entry Services provide catalog data entry services for your website including all the processing and conversion services. The skilled and trained professionals can take the hard copies of your product catalogs and then the required and necessary information is updated into the online catalog. After this, the products are placed in the appropriate classes/ sub-classes and then finally your products are displayed on the website.

The need of Online Catalogs

Your potential customers find it easy to browse through the list of your products across different categories if you have displayed the products through online Product catalogs. These services help many organizations to generate sales to a great extent.

Outsource and conversion is a good idea

Outsourcing Catalog Data Entry Services eliminates the need for having an in-house staff for catalog data entry processing and data conversion processes. You can ask for long term or short-term project according to your needs.

Number of advantages your company can enjoy after hiring these services. A few of them are given below:

1. Quick Turnaround Time- Their experienced team helps you to get a quick turnaround. Their talent and skills help them to improve the turnaround time of your organization.

2. Unique Quality- These services offer you 99% quality assurance. The quality assurance team evaluates the entire data to assure accuracy and quality.

3. Superior Communication - They help you to maintain consistent communication with your clients. They make it sure that they fully understand the limitations of each project. These services always aim to enrich the customer's experience.

4. Safety of Data - Safety of your data is ensured by these services. Security protocols are used and the utmost level of protection is provided for your data. Utilization of Virtual Private Network (VPN) is done as a secure method for accessing your data.

5. Customized Solutions - These Data Entry Services understand that every business follows different strategies and are unique in their own way. They adapt their services to fulfill the specific needs of every individual project. The deep-domain expertise from these services offers you positive solutions in every industry vertical.

Aashima is a content writer at SuntecIndia; Provider Company specialized in Data entry services. She is a very dedicated writer of our company and always tries to give the best possible solutions for outsourcing Data entry services.


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The Benefits of Data Mining

Data mining can truly help a business reach its fullest potential. It is a way to assess how business is being affected by certain characteristics, and can help business owners increase their profits and avoid making business mistakes down the line. Essentially, through this process, a business is analyzing certain data from different perspectives in order to get a full rounded view of how their company is doing. Business owners can get a broad perspective on things such as customer trending, where they are losing money and where they are making money. The information can also reveal ways that can help a business cut unneeded costs and can help them increase their overall income.

Data mining software is one tool that can help a company assess and analyze their data in more efficient terms. It can be extremely user friendly and allow people to delve into their data from a variety of different angles and points of view. In more technical terms, data mining software allows you to see the correlations and patterns of one's own data compared with those across many other regional databases.

People have been using data mining for many years in different formats. Only since the technology has become available has data software been used. But there have been many ways in the past for companies to assess their data and use it to their advantage. By taking polls, or using store scanners, product codes and bar codes, people have been able to gather data, analyze it and use it to their advantage. But it cannot be denied that the availability of greater technology has greatly increased the ability to store or gather data, make predictions about outcomes and use customer trend reports to greater advantages. The ability to store infinite amounts of data has given business owners a great advantage and truly has helped increase sales and lower costs. This data mining has actually led to data being stored in data warehouses. In data warehouses, various organizations will integrate their mined data into one large data warehouse. The information accessible in data warehouses is available to further help companies reduce risk taking and integrate proper selling techniques to improve business.

Data mining also can allow companies to see where their best selling points are and give them the opportunity to take advantage of this information. For example, if a pharmacy places a display of lip balm at the cashier counter, data mining can detect how many people bought lip balm from the cashier counter rather people who bought the lip balm when it was placed at another point in the store. Data mining can determine where the most effective points of sale are throughout a store or if a certain promotion went well one time of the month, but did not go well at another time of the month. Companies can make offers based on the buying habits of their customers as well.

Data mining can truly help businesses reach their highest profitability by paying attention to customer trending.


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Data Mining and Financial Data Analysis

Most marketers understand the value of collecting financial data, but also realize the challenges of leveraging this knowledge to create intelligent, proactive pathways back to the customer. Data mining - technologies and techniques for recognizing and tracking patterns within data - helps businesses sift through layers of seemingly unrelated data for meaningful relationships, where they can anticipate, rather than simply react to, customer needs as well as financial need. In this accessible introduction, we provides a business and technological overview of data mining and outlines how, along with sound business processes and complementary technologies, data mining can reinforce and redefine for financial analysis.

Objective:

1. The main objective of mining techniques is to discuss how customized data mining tools should be developed for financial data analysis.

2. Usage pattern, in terms of the purpose can be categories as per the need for financial analysis.

3. Develop a tool for financial analysis through data mining techniques.

Data mining:

Data mining is the procedure for extracting or mining knowledge for the large quantity of data or we can say data mining is "knowledge mining for data" or also we can say Knowledge Discovery in Database (KDD). Means data mining is : data collection , database creation, data management, data analysis and understanding.

There are some steps in the process of knowledge discovery in database, such as

1. Data cleaning. (To remove nose and inconsistent data)

2. Data integration. (Where multiple data source may be combined.)

3. Data selection. (Where data relevant to the analysis task are retrieved from the database.)

4. Data transformation. (Where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations, for instance)

5. Data mining. (An essential process where intelligent methods are applied in order to extract data patterns.)

6. Pattern evaluation. (To identify the truly interesting patterns representing knowledge based on some interesting measures.)

7. Knowledge presentation.(Where visualization and knowledge representation techniques are used to present the mined knowledge to the user.)

Data Warehouse:

A data warehouse is a repository of information collected from multiple sources, stored under a unified schema and which usually resides at a single site.

Text:

Most of the banks and financial institutions offer a wide verity of banking services such as checking, savings, business and individual customer transactions, credit and investment services like mutual funds etc. Some also offer insurance services and stock investment services.

There are different types of analysis available, but in this case we want to give one analysis known as "Evolution Analysis".

Data evolution analysis is used for the object whose behavior changes over time. Although this may include characterization, discrimination, association, classification, or clustering of time related data, means we can say this evolution analysis is done through the time series data analysis, sequence or periodicity pattern matching and similarity based data analysis.

Data collect from banking and financial sectors are often relatively complete, reliable and high quality, which gives the facility for analysis and data mining. Here we discuss few cases such as,

Eg, 1. Suppose we have stock market data of the last few years available. And we would like to invest in shares of best companies. A data mining study of stock exchange data may identify stock evolution regularities for overall stocks and for the stocks of particular companies. Such regularities may help predict future trends in stock market prices, contributing our decision making regarding stock investments.

Eg, 2. One may like to view the debt and revenue change by month, by region and by other factors along with minimum, maximum, total, average, and other statistical information. Data ware houses, give the facility for comparative analysis and outlier analysis all are play important roles in financial data analysis and mining.

Eg, 3. Loan payment prediction and customer credit analysis are critical to the business of the bank. There are many factors can strongly influence loan payment performance and customer credit rating. Data mining may help identify important factors and eliminate irrelevant one.

Factors related to the risk of loan payments like term of the loan, debt ratio, payment to income ratio, credit history and many more. The banks than decide whose profile shows relatively low risks according to the critical factor analysis.

We can perform the task faster and create a more sophisticated presentation with financial analysis software. These products condense complex data analyses into easy-to-understand graphic presentations. And there's a bonus: Such software can vault our practice to a more advanced business consulting level and help we attract new clients.

To help us find a program that best fits our needs-and our budget-we examined some of the leading packages that represent, by vendors' estimates, more than 90% of the market. Although all the packages are marketed as financial analysis software, they don't all perform every function needed for full-spectrum analyses. It should allow us to provide a unique service to clients.

The Products:

ACCPAC CFO (Comprehensive Financial Optimizer) is designed for small and medium-size enterprises and can help make business-planning decisions by modeling the impact of various options. This is accomplished by demonstrating the what-if outcomes of small changes. A roll forward feature prepares budgets or forecast reports in minutes. The program also generates a financial scorecard of key financial information and indicators.

Customized Financial Analysis by BizBench provides financial benchmarking to determine how a company compares to others in its industry by using the Risk Management Association (RMA) database. It also highlights key ratios that need improvement and year-to-year trend analysis. A unique function, Back Calculation, calculates the profit targets or the appropriate asset base to support existing sales and profitability. Its DuPont Model Analysis demonstrates how each ratio affects return on equity.

Financial Analysis CS reviews and compares a client's financial position with business peers or industry standards. It also can compare multiple locations of a single business to determine which are most profitable. Users who subscribe to the RMA option can integrate with Financial Analysis CS, which then lets them provide aggregated financial indicators of peers or industry standards, showing clients how their businesses compare.

iLumen regularly collects a client's financial information to provide ongoing analysis. It also provides benchmarking information, comparing the client's financial performance with industry peers. The system is Web-based and can monitor a client's performance on a monthly, quarterly and annual basis. The network can upload a trial balance file directly from any accounting software program and provide charts, graphs and ratios that demonstrate a company's performance for the period. Analysis tools are viewed through customized dashboards.

PlanGuru by New Horizon Technologies can generate client-ready integrated balance sheets, income statements and cash-flow statements. The program includes tools for analyzing data, making projections, forecasting and budgeting. It also supports multiple resulting scenarios. The system can calculate up to 21 financial ratios as well as the breakeven point. PlanGuru uses a spreadsheet-style interface and wizards that guide users through data entry. It can import from Excel, QuickBooks, Peachtree and plain text files. It comes in professional and consultant editions. An add-on, called the Business Analyzer, calculates benchmarks.

ProfitCents by Sageworks is Web-based, so it requires no software or updates. It integrates with QuickBooks, CCH, Caseware, Creative Solutions and Best Software applications. It also provides a wide variety of businesses analyses for nonprofits and sole proprietorships. The company offers free consulting, training and customer support. It's also available in Spanish.

ProfitSystem fx Profit Driver by CCH Tax and Accounting provides a wide range of financial diagnostics and analytics. It provides data in spreadsheet form and can calculate benchmarking against industry standards. The program can track up to 40 periods.


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Advantageous Data Entry Services in Era of Globalization

Data generally represent the information and can be defined with numbers or alphabetical symbols. Data entry can be determined as process that converts data from one form to another one. Such solutions usually includes almost all business fields and professional services, such as data conversion, offline data entry work, data processing, image processing, data entry outsourcing, data mining etc. One has to collect data on various topics and have to represent them in some meaningful manner.

There are several tasks for data entry services. It may includes data-entry into websites, tracking debit or credit card transactions, entry into electronic books, image formatting, keeping hard copy of office applications for scanning or printing, database for mails, use of data entry software as well as management of all these activities. In addition some time consuming tasks such as entering data in offline mode to track websites, gathering effective websites, which may need for consultation and to fill online forms. One of the good examples of data entry tasks is writing the image. You have to enter the images to incorporate pictures and attachments in magazines, e Books and white papers. Scanned images also needed to enter the details on the file. Another example of data-entry work is insurance claim. Insurance firms file a claim for insurance in process to get the cost of services. All systems for payment, form processing and insurance claims are followed by data entry services.

Data processing is also very useful tasks needed to be managed, regardless of company size or complexity. You have to follow some methods in order to accomplish your data processing tasks accurately. Such services help firms in terms of clear analysis of activities, policies, strategies and actions. Data processing and other services like data cleaning, image processing, OCR clean up, survey processing are related to provide a well-processed and complete data which can be used to get simple explanation of data.

There are plenty of advantages such services. For example data conversion is process which is very significant for any firm to drive their business powerfully. Data conversion can be considered as transfer of data from one format to another. There are also some other useful services like data transformation and many other which directly or indirectly essential for smooth functionality of any business.

Be advantageous in this competitive environment by choosing the right business services for benefits of yours and your organization.



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Limitations and Challenges in Effective Web Data Mining

Web data mining and data collection is critical process for many business and market research firms today. Conventional Web data mining techniques involve search engines like Google, Yahoo, AOL, etc and keyword, directory and topic-based searches. Since the Web's existing structure cannot provide high-quality, definite and intelligent information, systematic web data mining may help you get desired business intelligence and relevant data.

Factors that affect the effectiveness of keyword-based searches include:
• Use of general or broad keywords on search engines result in millions of web pages, many of which are totally irrelevant.
• Similar or multi-variant keyword semantics my return ambiguous results. For an instant word panther could be an animal, sports accessory or movie name.
• It is quite possible that you may miss many highly relevant web pages that do not directly include the searched keyword.

The most important factor that prohibits deep web access is the effectiveness of search engine crawlers. Modern search engine crawlers or bot can not access the entire web due to bandwidth limitations. There are thousands of internet databases that can offer high-quality, editor scanned and well-maintained information, but are not accessed by the crawlers.

Almost all search engines have limited options for keyword query combination. For example Google and Yahoo provide option like phrase match or exact match to limit search results. It demands for more efforts and time to get most relevant information. Since human behavior and choices change over time, a web page needs to be updated more frequently to reflect these trends. Also, there is limited space for multi-dimensional web data mining since existing information search rely heavily on keyword-based indices, not the real data.

Above mentioned limitations and challenges have resulted in a quest for efficiently and effectively discover and use Web resources. Send us any of your queries regarding Web Data mining processes to explore the topic in more detail.


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Data Mining Prevention by Poker Sites or What to do About WrecklessJoe55

As the ingenuity of third party program designers continues to challenge poker sites that need to ensure security for their users, along comes an upstart poker site that has changed one simple rule which could essentially solve a lot of problems for any player concerned about their long term statistics being examined by ruthless competitors.

Firstly though, let's define data mining for those who may not be sure what it is exactly. Data mining is the exchange of shared profiling information amongst a community of other players. As a player on most any online poker sites, it's quite likely you have been tracked through banned programs like Poker Sherlock or Poker Edge or had your information handed over via hand histories in another program called Poker Tracker. Although Poker Stars and Party Poker make this much more difficult (scanning your hard drive for such software) there are round-about tricks that enable them to work but you wouldn't want to describe them as smooth by any means.

Now the advantage of having access to a shared database of information about opponents is that if you happened to join an online table using this software, one or some of your opponents may be displayed via HUD some valuable statistics that may help your decision making during hand. Let's say for example that you are in a hand with a player named WrecklessJoe55. You are holding Th9h and the board shows Jc8cQc Ac and 2d. There is a big river bet put to you for the remainder of your stack to call. We will ignore the odds situation here for now, because either way, it's not the easiest call in the world.

Now let's say that through a purchased exchange of 100,000 hand histories via Poker Tracker you actually have some historical information on WrecklessJoe55 which clearly makes him a maniacal LAG player. Well that information would be leading towards a call. Just the opposite, if WrecklessJoe55 had a VPIP of 11% and PFR% of 7% along with a WSDW% of 72%, then these TAG statistics would be leading toward a fold - in fact I'd be almost sure of it.

The disdain poker sites have for these types of software is that you have never played with WrecklessJoe55 and you shouldn't know that information until YOU have ascertained it, not someone else. Yes, just like a regular live poker room. The Poker Stars security staff basically once told me that that is the guideline with which they want to emulate and all security policy emanates from that thinking.

Now we get to Cake Poker, an upstart network that is actually accepting USA player online! They came up with a policy that would essentially crush the inherent value in any data-mining program. It's rather simple too, as stated on the Cake Poker website:

"CakePoker players will be granted the option of changing their Poker Nickname every 7 Days. By allowing players to change their Poker Nickname often, CakePoker thus negates the effectiveness of shared or prolonged poker data tracking."

I wonder how much time and resources Poker Stars and Party Poker would save in their overall security budget if they adopted the same policy? Allow the players to change their name! It's simple! Big kudos to CakePoker for allowing this defence, in the name of protecting its players. Now although it no longer emulates a real live poker room, it definitely makes for a level playing field, and that's something to think about for the major players to be sure.


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Data Extraction Services - A Helpful Hand For Large Organization

The data extraction is the way to extract and to structure data from not structured and semi-structured electronic documents, as found on the web and in various data warehouses. Data extraction is extremely useful for the huge organizations which deal with considerable amounts of data, daily, which must be transformed into significant information and be stored for the use this later on.

Your company with tons of data but it is difficult to control and convert the data into useful information. Without right information at the right time and based on half of accurate information, decision makers with a company waste time by making wrong strategic decisions. In high competing world of businesses, the essential statistics such as information customer, the operational figures of the competitor and the sales figures inter-members play a big role in the manufacture of the strategic decisions. It can help you to take strategic business decisions that can shape your business' goals..

Outsourcing companies provide custom made services to the client's requirements. A few of the areas where it can be used to generate better sales leads, extract and harvest product pricing data, capture financial data, acquire real estate data, conduct market research , survey and analysis, conduct product research and analysis and duplicate an online database..

The different types of Data Extraction Services:

    Database Extraction:
    Reorganized data from multiple databases such as statistics about competitor's products, pricing and latest offers and customer opinion and reviews can be extracted and stored as per the requirement of company.
    Web Data Extraction:
    Web Data Extraction is also known as data Extraction which is usually referred to the practice of extract or reading text data from a targeted website.

Businesses have now realized about the huge benefits they can get by outsourcing their services. Then outsourcing is profitable option for business. Since all projects are custom based to suit the exact needs of the customer, huge savings in terms of time, money and infrastructure are among the many advantages that outsourcing brings.

Advantages of Outsourcing Data Extraction Services:

    Improved technology scalability
    Skilled and qualified technical staff who are proficient in English
    Advanced infrastructure resources
    Quick turnaround time
    Cost-effective prices
    Secure Network systems to ensure data safety
    Increased market coverage

By outsourcing, you can definitely increase your competitive advantages. Outsourcing of services helps businesses to manage their data effectively, which in turn would enable them to experience an increase in profits.


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Offshore Data Entry Is The Need Of The Day For Any Business

To run a business successfully means embracing a new challenge everyday. It indicates exciting avenues to be ventured into and daunting hurdles to be overcome while keeping ahead of competitors. Each day is a new day that needs to be met with new strategies, plans and goals. However certain crucial aspects of running a business can become monotonous and can require repetitive work on a regular basis but the accuracy needs to be impeccable. The data entry requirements of a company fall under this category of essential tasks that can be quite time consuming and repeatable but essential for running the business successfully. Business houses are therefore looking for options to get this task done smoothly without using up important resources of the company yet maintaining the required standard of accuracy and confidentiality. Offshore data entry is therefore fast becoming the preferred option of every business entity.

Offshore data entry is the process of hiring an external entity to perform the data entry functions for the business in a country besides the ones where the products and services of the business will be sold or used. The offshore data entry services provided by a vendor help the firm access processed and accurate data that has been well -presented to be of maximum use to the firm. The offshore data entry firm employees have the task of collecting data from written or printed records and entering them into the computer system. This data is maintained in a systematic manner to be as informative to the business as possible. The offshore data entry records are then transferred back to the client for regular referral and checking. Some of the major countries that are providing such offshore data entry services are India, China, Russia, Pakistan, Nepal, Bangladesh, Egypt, Malaysia and others.

The major criteria for a job to be qualified for offshore requirements are that the task should be repeatable, have high information content, be transferable over the internet, the process is easy to set up and the wages paid to the offshore data entry staff must be reasonably lower than those in the original country. The major requirement for offshore data entry services arises from the strong need to cut down on costs and the internet and the facilities it provides has given a direction to this need. Offshore data entry jobs have opened up a world of opportunities for professionals around the world and the constant advancement in the field of technology and internet further add to the advantage.

The prevalent exposure to internet has enabled many freelancers across the globe to offer their services for offshore data entry to small businesses and this works out to be a winning deal for both the parties involved. Free trade advocates are vocal about their support for offshore data entry business as they feel that this will provide benefits to economies as a whole in the form of labor off shoring. Whatever may be the reason for a company to employ offshore assistance, but the fact remains that in today's world offshore data entry is a booming business and the trend definitely seems to be on an upward motion.


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Accelerating Accumulated Data Mining

We all have heard of Data Mining and we have all seen the abilities it can produce, but we also know how tedious the collection of data can be. It is the same for a little small company with a few customers as it is for a large company with millions of customers. Additionally how do you keep your data safe?

We have all heard of Identity Theft and the importance of secure data. But just because we have spent millions of dollars in IT work does not mean we know it is accurate? Things change fast you see; people get new telephone numbers, change addresses and jobs at least one of the three every three years. The chances of any database having accurate information is simply not possible.

Thus if we are data mining we need a way to verify which data sets are accurate and believe it or not the last set of data may not be the most accurate therefore we cannot simply discard the old data for the new data you see? We need ways to accelerate the accumulated data so we can run through it as fast as possible yet we must insure that our data mining techniques are taking into consideration miss matched data and incorrect data, along with inaccurate data.

Data Mining may have been over hyped a little and those business systems or even government data mining systems at the NSA; if they do not take into consideration these thoughts they are basically worthless and should not be considered you see? Think on this in 2006.


Source: http://ezinearticles.com/?Accelerating-Accumulated-Data-Mining&id=202738

Benefits and Advantages of Data Mining

One definition given to data mining is the categorization of information according to the needs and preferences of the user. In data mining, you try to find patterns within a big volume of available data. It is a potent and popular technology for different industries. Data mining can even be compared to the difficult task of looking for a needle in the haystack. The greatest challenge is not obtaining information but uncovering connections and information that have not been known in the past.

Yet, data mining tools can only be utilized efficiently provided you possess huge amounts of information in repository. Almost all of corporate organizations already hold this information. One good example is the list of potential clients for marketing purposes. These are the consumers to whom you can sell commodities or services. You have greater chances of generating more revenues if you know these potential customers in the inventory and determine consumption behavior. There are benefits that you need to know regarding data mining.

    Data mining is not only for entrepreneurs. The process is cut out for analysis as well and can be employed by government agencies, non-profit organizations, and basketball teams. In short, the data must be made more specific and refined according to the needs of the group concerned.

    This unique method can be used along with demographics. Data mining combined with demographics enables enterprises to pursue the advertising strategy for specific segments of customers. That form of advertising that is related directly to behavior.

    It has a flexible nature and can be used by business organizations that focus on the needs of customers. Data mining is one of the more relevant services because of the fast-paced and instant access to information together with techniques in economic processing.

However, you need to prepare ahead of time the data used for mining. It is essential to understand the principles of clustering and segmentation. These two elements play a vital part in marketing campaigns and customer interface. These components encompass the purchasing conduct of consumers over a particular duration. You will be able to separate your customers into categories based on the earnings brought to your company. It is possible to determine the income that these customers will generate and retention opportunities. Simply remember that nearly all profit-oriented entities will desire to maintain high-value and low-risk clients. The target is to ensure that these customers keep on buying for the long-term.


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Web Data Extraction Services and Data Collection Form Website Pages

For any business market research and surveys plays crucial role in strategic decision making. Web scrapping and data extraction techniques help you find relevant information and data for your business or personal use. Most of the time professionals manually copy-paste data from web pages or download a whole website resulting in waste of time and efforts.

Instead, consider using web scraping techniques that crawls through thousands of website pages to extract specific information and simultaneously save this information into a database, CSV file, XML file or any other custom format for future reference.

Examples of web data extraction process include:
• Spider a government portal, extracting names of citizens for a survey
• Crawl competitor websites for product pricing and feature data
• Use web scraping to download images from a stock photography site for website design

Automated Data Collection
Web scraping also allows you to monitor website data changes over stipulated period and collect these data on a scheduled basis automatically. Automated data collection helps you discover market trends, determine user behavior and predict how data will change in near future.

Examples of automated data collection include:
• Monitor price information for select stocks on hourly basis
• Collect mortgage rates from various financial firms on daily basis
• Check whether reports on constant basis as and when required

Using web data extraction services you can mine any data related to your business objective, download them into a spreadsheet so that they can be analyzed and compared with ease.

In this way you get accurate and quicker results saving hundreds of man-hours and money!

With web data extraction services you can easily fetch product pricing information, sales leads, mailing database, competitors data, profile data and many more on a consistent basis.


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Data Mining

Data mining is the retrieving of hidden information from data using algorithms. Data mining helps to extract useful information from great masses of data, which can be used for making practical interpretations for business decision-making. It is basically a technical and mathematical process that involves the use of software and specially designed programs. Data mining is thus also known as Knowledge Discovery in Databases (KDD) since it involves searching for implicit information in large databases. The main kinds of data mining software are: clustering and segmentation software, statistical analysis software, text analysis, mining and information retrieval software and visualization software.

Data mining is gaining a lot of importance because of its vast applicability. It is being used increasingly in business applications for understanding and then predicting valuable information, like customer buying behavior and buying trends, profiles of customers, industry analysis, etc. It is basically an extension of some statistical methods like regression. However, the use of some advanced technologies makes it a decision making tool as well. Some advanced data mining tools can perform database integration, automated model scoring, exporting models to other applications, business templates, incorporating financial information, computing target columns, and more.

Some of the main applications of data mining are in direct marketing, e-commerce, customer relationship management, healthcare, the oil and gas industry, scientific tests, genetics, telecommunications, financial services and utilities. The different kinds of data are: text mining, web mining, social networks data mining, relational databases, pictorial data mining, audio data mining and video data mining.

Some of the most popular data mining tools are: decision trees, information gain, probability, probability density functions, Gaussians, maximum likelihood estimation, Gaussian Baves classification, cross-validation, neural networks, instance-based learning /case-based/ memory-based/non-parametric, regression algorithms, Bayesian networks, Gaussian mixture models, K-Means and hierarchical clustering, Markov models, support vector machines, game tree search and alpha-beta search algorithms, game theory, artificial intelligence, A-star heuristic search, HillClimbing, simulated annealing and genetic algorithms.

Some popular data mining software includes: Connexor Machines, Copernic Summarizer, Corpora, DocMINER, DolphinSearch, dtSearch, DS Dataset, Enkata, Entrieva, Files Search Assistant, FreeText Software Technologies, Intellexer, Insightful InFact, Inxight, ISYS:desktop, Klarity (part of Intology tools), Leximancer, Lextek Onix Toolkit, Lextek Profiling Engine, Megaputer Text Analyst, Monarch, Recommind MindServer, SAS Text Miner, SPSS LexiQuest, SPSS Text Mining for Clementine, Temis-Group, TeSSI®, Textalyser, TextPipe Pro, TextQuest, Readware, Quenza, VantagePoint, VisualText(TM), by TextAI, Wordstat. There is also free software and shareware such as INTEXT, S-EM (Spy-EM), and Vivisimo/Clusty.



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