Data Mining
Content
DATA MINING
Data mining, also known as knowledge-discovery in databases (KDD), is the practice of automatically searching large stores of data for patterns. To do this, data mining uses computational techniques from statistics and pattern recognition.
Definition
Data mining has been defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data" and "The science of extracting useful information from large data sets or databases" . Although it is usually used in relation to analysis of data, data mining, like artificial intelligence, is an umbrella term and is used with varied meaning in a wide range of contexts. It is usually associated with a business or organization's need to identify trends. A simple example of data mining is its use in a retail sales department. If a store tracks the purchases of a customer and notices that a customer buys a lot of silk shirts, the data mining system will make a correlation between that customer and silk shirts. The sales department will look at that information and may begin direct mail marketing of silk shirts to that customer, or it may alternatively attempt to get the customer to buy a wider range of products. In this case, the data mining system used by the retail store discovered new information about the customer that was previously unknown to the company. Another widely used (though hypothetical) example is that of a very large North American chain of supermarkets. Through intensive analysis of the transactions and the goods bought over a period of time, analysts found that beers and diapers were often bought together. Though explaining this interrelation might be difficult, taking advantage of it, on the other hand, should not be hard (e.g. placing the high-profit diapers next to the high-profit beers). This technique is often referred to as "Market Basket Analysis". In statistical analyses in which there is no underlying theoretical model, data mining is often approximated via stepwise regression methods wherein the space of 2k possible relationships between a single outcome variable and k potential explanatory variables is smartly searched. With the advent of grid computing, it became possible (when k is less than approximately 40) to examine all 2k models. This procedure is called all subsets or exhaustive regression. Some of the first applications of exhaustive regression involved the study of clinical data.
Data dredging
Used in the technical context of data warehousing and analysis, the term "data mining" is neutral. However, it sometimes has a more pejorative usage that implies imposing patterns (and particularly causal relationships) on data where none exist. This imposition of irrelevant, misleading or trivial attribute correlation is more properly criticized as "data dredging" in the statistical literature. Another term for this misuse of statistics is data fishing. Used in this latter sense, data dredging implies scanning the data for any relationships, and then when one is found coming up with an interesting explanation. (This is also referred to as "over fitting the model".) The problem is that large data sets invariably happen to have some exciting relationships peculiar to that data. Therefore any conclusions reached are likely to be highly suspect. In spite of this, some exploratory data work is always required in any applied statistical analysis to get a feel for the data, so sometimes the line between good statistical practice and data dredging is less than clear. The common approach, in data mining, to overcoming the problem of over fitting is to separate the data into two or three separate data sets (called the training set, validation set, and testing set). The model is built using the training and validation set, and is then tested using the testing set; the procedure can be repeated many times by re sampling the data sets, in order to be more certain that a real pattern has been found and that the model is not merely capitalizing on random chance (i.e. over fitting). A more significant danger is finding correlations that do not really exist. Investment analysts appear to be particularly vulnerable to this. "There have always been a considerable number of pathetic people who busy themselves examining the last thousand numbers which have appeared on a roulette wheel, in search of some repeating pattern. Sadly enough, they have usually found it.". However, when properly done, determining correlations in Investment analysis has proven to be very profitable for statistical arbitrage operations (such as pairs trading strategies), and furthermore correlation analysis has shown to be very useful in risk management. Indeed, finding correlations in the financial markets, when done properly, is not the same as finding false patterns in roulette wheels. Most data mining efforts are focused on developing a finely-grained, highly detailed model of some large data set. Other researchers have described an alternate method that involves finding the minimal differences between elements in a data set, with the goal of developing simpler models that represent relevant data.
Privacy concerns
There are also privacy concerns associated with data mining. For example, if an employer has access to medical records, they may screen out people who have diabetes or have had a heart attack. Screening out such employees will cut costs for insurance, but it creates ethical and legal problems.
Data mining government or commercial data sets for national security or law enforcement purposes has also raised privacy concerns. There are many legitimate uses of data mining. For example, a database of prescription drugs taken by a group of people could be used to find combinations of drugs with adverse reactions. Since the combination may occur in only 1 out of 1000 people, a single case may not be apparent. A project involving pharmacies could reduce the number of drug reactions and potentially save lives. Unfortunately, there is also a huge potential for abuse of such a database. Essentially, data mining gives information that would not be available otherwise. It must be properly interpreted to be useful. When the data collected involves individual people, there are many questions concerning privacy, legality, and ethics.
Combinatorial game data mining
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Data mining from combinatorial game oracles:
Since the early 1990's, with the availability of oracles for certain combinatorial games, also called tablebases (e.g. for 3x3-chess) with any beginning configuration, small-board dots-and-boxes, small-board-hex, and certain endgames in chess, dots-and-boxes, and hex; a new area for data mining has been opened up. This is the extraction of humanusable strategies from these oracles. This is pattern-recognition at too high an abstraction for known Statistical Pattern Recognition algorithms or any other algorithmic approaches to be applied: at least, no one knows how to do it yet (as of January 2005). The method used is the full force of Scientific Method: extensive experimentation with the table bases combined with intensive study of table base-answers to well designed problems, combined with knowledge of prior art i.e. pre-table base knowledge, leading to flashes of insight. Berlekamp in dots-and-boxes etc. and John Nunn in chess endgames are notable examples of people doing this work, though they were not and are not involved in tablebase generation.
Data Mining Improves Decision Making Data mining uncovers patterns in data using predictive techniques. These patterns play a critical role in decision making because they reveal areas for process improvement. Using data mining, organizations can increase the profitability of their interactions with customers, detect fraud, and improve risk management. The patterns uncovered using data mining help organizations make better and timelier decisions.
Relational data mining
Relational data mining is a data mining technique for relational databases. Unlike traditional data mining algorithms, which look for patterns in a single table (propositional patterns), relational data mining algorithms look for patterns among multiple tables (relational patterns). For most types of propositional propatterns, there are corresponding relational patterns. For example, there are relational classification rules, relational regression trees, relational association rules, and so on. The most important theoretical foundation of relational data mining is inductive logic programming. DATA MINING FUTURE Data mining is the analysis of large data sets to discover patterns of interests. Data mining has come a long way from the early academic beginnings in the late seventies. Many of the early data mining software packages were based on one algorithm. Until the mid-nineties data mining required considerable specialized knowledge and was mainly restricted to statisticians. Customer Relationship Management (CRM) software played a great part in popularizing data mining among corporate users. Data mining in CRMs are often hidden from the end users. The algorithms are packaged behind business functionality such as Churn analysis. Churn analysis is the process to predict which customers are the ones most likely to defect to a competitor. Data mining algorithms are now freely available. Database vendors have started to incorporate data mining modules. Developers can now access data mining via open standards such as OLE-DB for data mining on SQL Server 2000. Data mining functionality can now be added directly to the application source code. The complexity of data mining must be hidden from end-users before it will take the true center stage in an organization. Business use cases can be designed, with tight constrains , around data mining algorithms.
Data mining, also known as knowledge-discovery in databases (KDD), is the practice of automatically searching large stores of data for patterns. To do this, data mining uses computational techniques from statistics and pattern recognition.
Definition
Data mining has been defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data" and "The science of extracting useful information from large data sets or databases" . Although it is usually used in relation to analysis of data, data mining, like artificial intelligence, is an umbrella term and is used with varied meaning in a wide range of contexts. It is usually associated with a business or organization's need to identify trends. A simple example of data mining is its use in a retail sales department. If a store tracks the purchases of a customer and notices that a customer buys a lot of silk shirts, the data mining system will make a correlation between that customer and silk shirts. The sales department will look at that information and may begin direct mail marketing of silk shirts to that customer, or it may alternatively attempt to get the customer to buy a wider range of products. In this case, the data mining system used by the retail store discovered new information about the customer that was previously unknown to the company. Another widely used (though hypothetical) example is that of a very large North American chain of supermarkets. Through intensive analysis of the transactions and the goods bought over a period of time, analysts found that beers and diapers were often bought together. Though explaining this interrelation might be difficult, taking advantage of it, on the other hand, should not be hard (e.g. placing the high-profit diapers next to the high-profit beers). This technique is often referred to as "Market Basket Analysis". In statistical analyses in which there is no underlying theoretical model, data mining is often approximated via stepwise regression methods wherein the space of 2k possible relationships between a single outcome variable and k potential explanatory variables is smartly searched. With the advent of grid computing, it became possible (when k is less than approximately 40) to examine all 2k models. This procedure is called all subsets or exhaustive regression. Some of the first applications of exhaustive regression involved the study of clinical data.
Data dredging
Used in the technical context of data warehousing and analysis, the term "data mining" is neutral. However, it sometimes has a more pejorative usage that implies imposing patterns (and particularly causal relationships) on data where none exist. This imposition of irrelevant, misleading or trivial attribute correlation is more properly criticized as "data dredging" in the statistical literature. Another term for this misuse of statistics is data fishing. Used in this latter sense, data dredging implies scanning the data for any relationships, and then when one is found coming up with an interesting explanation. (This is also referred to as "over fitting the model".) The problem is that large data sets invariably happen to have some exciting relationships peculiar to that data. Therefore any conclusions reached are likely to be highly suspect. In spite of this, some exploratory data work is always required in any applied statistical analysis to get a feel for the data, so sometimes the line between good statistical practice and data dredging is less than clear. The common approach, in data mining, to overcoming the problem of over fitting is to separate the data into two or three separate data sets (called the training set, validation set, and testing set). The model is built using the training and validation set, and is then tested using the testing set; the procedure can be repeated many times by re sampling the data sets, in order to be more certain that a real pattern has been found and that the model is not merely capitalizing on random chance (i.e. over fitting). A more significant danger is finding correlations that do not really exist. Investment analysts appear to be particularly vulnerable to this. "There have always been a considerable number of pathetic people who busy themselves examining the last thousand numbers which have appeared on a roulette wheel, in search of some repeating pattern. Sadly enough, they have usually found it.". However, when properly done, determining correlations in Investment analysis has proven to be very profitable for statistical arbitrage operations (such as pairs trading strategies), and furthermore correlation analysis has shown to be very useful in risk management. Indeed, finding correlations in the financial markets, when done properly, is not the same as finding false patterns in roulette wheels. Most data mining efforts are focused on developing a finely-grained, highly detailed model of some large data set. Other researchers have described an alternate method that involves finding the minimal differences between elements in a data set, with the goal of developing simpler models that represent relevant data.
Privacy concerns
There are also privacy concerns associated with data mining. For example, if an employer has access to medical records, they may screen out people who have diabetes or have had a heart attack. Screening out such employees will cut costs for insurance, but it creates ethical and legal problems.
Data mining government or commercial data sets for national security or law enforcement purposes has also raised privacy concerns. There are many legitimate uses of data mining. For example, a database of prescription drugs taken by a group of people could be used to find combinations of drugs with adverse reactions. Since the combination may occur in only 1 out of 1000 people, a single case may not be apparent. A project involving pharmacies could reduce the number of drug reactions and potentially save lives. Unfortunately, there is also a huge potential for abuse of such a database. Essentially, data mining gives information that would not be available otherwise. It must be properly interpreted to be useful. When the data collected involves individual people, there are many questions concerning privacy, legality, and ethics.
Combinatorial game data mining
•
Data mining from combinatorial game oracles:
Since the early 1990's, with the availability of oracles for certain combinatorial games, also called tablebases (e.g. for 3x3-chess) with any beginning configuration, small-board dots-and-boxes, small-board-hex, and certain endgames in chess, dots-and-boxes, and hex; a new area for data mining has been opened up. This is the extraction of humanusable strategies from these oracles. This is pattern-recognition at too high an abstraction for known Statistical Pattern Recognition algorithms or any other algorithmic approaches to be applied: at least, no one knows how to do it yet (as of January 2005). The method used is the full force of Scientific Method: extensive experimentation with the table bases combined with intensive study of table base-answers to well designed problems, combined with knowledge of prior art i.e. pre-table base knowledge, leading to flashes of insight. Berlekamp in dots-and-boxes etc. and John Nunn in chess endgames are notable examples of people doing this work, though they were not and are not involved in tablebase generation.
Data Mining Improves Decision Making Data mining uncovers patterns in data using predictive techniques. These patterns play a critical role in decision making because they reveal areas for process improvement. Using data mining, organizations can increase the profitability of their interactions with customers, detect fraud, and improve risk management. The patterns uncovered using data mining help organizations make better and timelier decisions.
Relational data mining
Relational data mining is a data mining technique for relational databases. Unlike traditional data mining algorithms, which look for patterns in a single table (propositional patterns), relational data mining algorithms look for patterns among multiple tables (relational patterns). For most types of propositional propatterns, there are corresponding relational patterns. For example, there are relational classification rules, relational regression trees, relational association rules, and so on. The most important theoretical foundation of relational data mining is inductive logic programming. DATA MINING FUTURE Data mining is the analysis of large data sets to discover patterns of interests. Data mining has come a long way from the early academic beginnings in the late seventies. Many of the early data mining software packages were based on one algorithm. Until the mid-nineties data mining required considerable specialized knowledge and was mainly restricted to statisticians. Customer Relationship Management (CRM) software played a great part in popularizing data mining among corporate users. Data mining in CRMs are often hidden from the end users. The algorithms are packaged behind business functionality such as Churn analysis. Churn analysis is the process to predict which customers are the ones most likely to defect to a competitor. Data mining algorithms are now freely available. Database vendors have started to incorporate data mining modules. Developers can now access data mining via open standards such as OLE-DB for data mining on SQL Server 2000. Data mining functionality can now be added directly to the application source code. The complexity of data mining must be hidden from end-users before it will take the true center stage in an organization. Business use cases can be designed, with tight constrains , around data mining algorithms.
