data mining

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data mining

[′dad·ə ‚mīn·iŋ or dād·ə ‚mīn·iŋ]
(computer science)
The identification or extraction of relationships and patterns from data using computational algorithms to reduce, model, understand, or analyze data.
The automated process of turning raw data into useful information by which intelligent computer systems sift and sort through data, with little or no help from humans, to look for patterns or to predict trends.

Data mining

The development of computational algorithms for the identification or extraction of structure from data. This is done in order to help reduce, model, understand, or analyze the data. Tasks supported by data mining include prediction, segmentation, dependency modeling, summarization, and change and deviation detection. Database systems have brought digital data capture and storage to the mainstream of data processing, leading to the creation of large data warehouses. These are databases whose primary purpose is to gain access to data for analysis and decision support. Traditional manual data analysis and exploration requires highly trained data analysts and is ineffective for high dimensionality (large numbers of variables) and massive data sets. See Database management system

A data set can be viewed abstractly as a set of records, each consisting of values for a set of dimensions (variables). While data records may exist physically in a database system in a schema that spans many tables, the logical view is of concern here. Databases with many dimensions pose fundamental problems that transcend query execution and optimization. A fundamental problem is query formulation: How is it possible to provide data access when a user cannot specify the target set exactly, as is required by a conventional database query language such as SQL (Structured Query Language)? Decision support queries are difficult to state. For example, which records are likely to represent fraud in credit card, banking, or telecommunications transactions? Which records are most similar to records in table A but dissimilar to those in table B? How many clusters (segments) are in a database and how are they characterized? Data mining techniques allow for computer-driven exploration of the data, hence admitting a more abstract model of interaction than SQL permits.

Data mining techniques are fundamentally data reduction and visualization techniques. As the number of dimensions grows, the number of possible combinations of choices for dimensionality reduction explodes. For an analyst exploring models, it is infeasible to go through the various ways of projecting the dimensions or selecting the right subsamples (reduction along columns and rows). Data mining is based on machine-based exploration of many of the possibilities before a selected reduced set is presented to the analyst for feedback.

data mining

Analysis of data in a database using tools which look for trends or anomalies without knowledge of the meaning of the data. Data mining was invented by IBM who hold some related patents.

Data mining may well be done on a data warehouse.

ShowCase STRATEGY is an example of a data mining tool.

data mining

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. See data miner, Web mining, text mining, OLAP, decision support system, EIS, data warehouse and slice and dice.

Doing It Automatically
This BusinessMiner analysis determined that the most influential factor common to non-profitable customers was their credit limit. (Image courtesy of SAP.)
References in periodicals archive ?
as to how the RMA calculated the $456 million figure, Gould said the agency was able to use the data-mining "to find out producers that were found to be filing fraudulent claims .
Law enforcement agencies can use data-mining technology to help them deploy their resources, including personnel, more effectively and proactively.
In response to the GAO report, the Center for Democracy and Technology and the Heritage Foundation released a report offering guidelines for developing and using data-mining technologies in ways that would preserve privacy.
The company is relying on data warehousing and data-mining techniques to assist in this effort, Hoffman said.
0 dramatically optimizes the data-mining process and can accelerate the use of partners' data-mining tools:
Toppan Printing's new marketing services will employ data-mining techniques to measure the results of marketing promotions.
Other major enhancements include an enhanced interface that allows multiple users to work on the same project simultaneously, integration with SAS/Warehouse Administrator(TM) software, and a feature called Reporter Node, which assembles data-mining results into HTML reports that can be viewed with any Web browser.
M99 is one of the most comprehensive data-mining conferences, offering value to persons from all industries and at all levels of experience," said 1998 presenter/attendee Thomas Brauch, marketing manager at Fireman's Fund Insurance Company in San Francisco.
Enterprise Miner software is the first software to offer a common environment for its primary users - business analysts, quantitative professionals, and IT - and is the first to address the entire data-mining process within one automated solution.
Enterprise Miner software, the first data-mining software to offer a common environment for business analysts, quantitative professionals, and IT, gives customers a higher ROI through the deployment of an entire SEMMA data-mining process (sample, explore, modify, model, assess) within one automated solution.
Available since April on Windows NT, Enterprise Miner software is the first to offer a common data-mining environment for business decision makers and analysts.
The most important data-mining business applications, such as classification and predictive modeling, can be accomplished using just CART," says Dan Steinberg, president of Salford Systems.