Exploring and analyzing detailed business transactions. It implies “digging through tons of data” to uncover patterns and relationships contained within the business activity and history. Data mining can be done manually by slicing and dicing the data until a pattern becomes obvious. Or, it can be done with programs that analyze the data automatically. Data mining has become an important part of customer relationship management (CRM). In order to better understand customer behavior and preferences, businesses use data mining to wade through the huge amounts of information gathered via the Web
The goal of this credit card analysis is to determine the most influential factors common to non-profitable customers. In this case, BusinessMiner from Business Objects determined that the credit limit had the greatest effect on profitability and prioritized the results in graphical form.
SOURCE:
http://www.answers.com/topic/data-mining
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VERY GOOD ARTICLE
November 2003 (Vol. 36, No. 11) pp. 22-29
Data Mining for Very Busy People
Tim Menzies, West Virginia University
Ying Hu, University of British Columbia
Abstract
Most modern businesses can access mountains of data electronically—the trick is effectively using that data. In practice, this means summarizing large data sets to find the data that really matters. Most data miners are zealous hunters seeking detailed summaries and generating extensive and lengthy descriptions. The authors take a different approach and assume that busy people don’t need—or can’t use—complex models. Rather, they want only the data they need to achieve the most benefits.Instead of finding extensive descriptions of things, their data mining tool hunts for a minimal difference set between things because they believe a list of essential differences is easier to read and understand than detailed descriptions.
From Wikipedia, the free encyclopedia
In it, the authors make the argument that accessing data is not the problem for the data mining community – the problem is ignoring the irrelevant data. The angle presented is to search large data sets to find the smallest subset of the most relevant data. According to the authors, the approach of “learning the least” as applied to some data set is a departure from the norm – they state that most data miners are typically concerned with discovering a data summary with fine-grained details.
The authors describe the TAR2 treatment learner, a data mining tool that searches for “the minimal difference set between things.” It is claimed that TAR2 produces data models that are simpler to understand by humans, because the models are presented as a list of essential differences instead of a highly detailed summary.
The TAR2 treatment learner, which is available at http://menzies.us/rx.html, takes a large amount of data and creates a few simple rules instead of the complex tree produced by many data miners. It uses the three major concepts of treatment learning: lift, minimum best support, and the small treatment effect.
What the authors claim TAR2 has to offer over other treatment learners is the use of superior heuristics in finding data treatments. The TAR2 uses three key heuristic tricks. First, TAR2 chunks continuous attributes into separate bins of values. For example, instead of having a range of continuous values, the data would instead be separated into small values, medium values, and high values. Second, TAR2 assumes the small treatment effect and always deals with a small number of attributes. Finally, the TAR2 looks only at treatments with high ranges. It assumes that people are only interested in seeing positive results.
To support their claims of being able to derive simplified and more representative models for large data sets, the authors present three case studies in the domains of software risk estimation, software inspection policies, and requirements engineering.
Menzies and Hu apply the TAR2 treatment learner to several data sets in these domains, and demonstrate improved results using their methods.
Data mining and treatment learning
The article also includes a sidebar in which Menzies and Hu also describe some of the common methods used in both data mining and treatment learning. These include decision tree learning, association rule learning, wrappers, genetic algorithms, and simulated annealing algorithms.
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http://en.wikipedia.org/wiki/Data_mining
Data mining is an innovative way of gaining new and valuable business insights by analyzing the information held in your company database. These insights can enable you to identify market niches, and they support and facilitate the making of well-informed business decisions. Essentially, data mining is a groundbreaking way to leverage the information that your company already has in order to plan a business strategy for the future.
Data mining uncovers this in-depth business intelligence by using advanced analytical and modeling techniques. With data mining, you can ask far more sophisticated questions of your data than you can with conventional querying methods. The information that data mining provides can lead to an immense improvement in the quality and dependability of business decision-making.
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Data Mining, also known as Knowledge-Discovery in Databases (KDD), is the process of automatically searching large volumes of data for patterns. Data Mining is a fairly recent and contemporary topic in computing. However, Data Mining applies many older computational techniques from statistics, machine learning and pattern recognition.
Definition
Data Mining can be defined as “The nontrivial extraction of implicit, previously unknown, and potentially useful information from data” [1] and “The science of extracting useful information from large data sets or databases” [2]. 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 other organization’s need to identify trends. Data mining involves the process of analysing data to show patterns or relationships; sorting through large amounts of data; and picking out pieces of relative information or patterns that occur e.g., picking out statistical information from some data
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 parallel 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 plant data.[3]
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 “overfitting 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.
One common approach to evaluating the fitness of a model generated via data mining techniques is called cross validation. Cross validation is a technique that produces an estimate of generalization error based on resampling. In simple terms, the general idea behind cross validation is that dividing the data into two or or more separate data subsets allows one subset to be used to evaluate the generalizeability of the model learned from the other data subset(s). A data subset used to build a model is called a training set; the evaluation data subset is called the test set. Common cross validation techniques include the holdout method, k-fold cross validation, and the leave-one-out method.
Another pitfall of using data mining is that it may lead to discovering correlations that may not exist. “There have always been a considerable number of 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.” [4]. 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 highly detailed models 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. [5]
Privacy concerns
There are also privacy concerns associated with data mining – specifically regarding the source of the data analyzed. 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. [6]
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 exhibiting harmful interactions. Since any particular combination may occur in only 1 out of 1000 people, a great deal of data would need to be examined to discover such an interaction. 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 1990s, with the availability of oracles for certain combinatorial games, also called tablebases (e.g. for 3×3-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 human-usable 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 tablebases combined with intensive study of tablebase-answers to well designed problems, combined with knowledge of prior art i.e. pre-tablebase 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.
Notable uses of data mining
Data mining has been cited as the method by which the U.S. Army unit Able Danger supposedly had identified the 9/11 attack leader, Mohamed Atta, and three other 9/11 hijackers as possible members of an al Qaeda cell operating in the U.S. more than a year before the attack.
See also: Able Danger, wikinews:U.S. Army intelligence had detected 9/11 terrorists year before, says officer.
As is the case for economic models which successfully predict 10 of the last 3 recessions, one must of course know which other names came up on the “possible members” list before being confident this was not an exercise in data dredging.
