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

(database)
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 ?
Business understanding: focuses on understanding the project objectives from the business perspective and transforming them into a data mining problem (domain) definition.
Data mining integrates complex information systems with an understanding of underlying business processes.
Data mining also allows agencies to consider multiple variables at one time and to add more weight to those considered most important to the decision at hand.
NAG is a 30-year old company dedicated to making cross-platform mathematical, statistical, data mining components and tools for developers as well as 3D visualization application development environments.
They may be ahead of us now with some other things, but I think the insurance industry has caught up and a lot of companies are now implementing data warehousing and data mining and trying to embed it into their processes.
Data Mining Techniques, Third Edition" covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results.
Clementine was the first data mining workbench aimed at business users and is consistently acknowledged by users and analysts as the leading visual rapid modeling environment for data mining.
We were able to use a data mining tool from Cognos that allowed them to sort through their data warehouse to profile their customers in such a way that they were able to find out which referring web sites gave them sales and which ones didn't.
1 "Magic Quadrant for Customer Data Mining, 1Q06," by Gareth Herschel, Gartner Inc.
Pentaho provides a full spectrum of open source Business Intelligence (BI) capabilities including reporting, analysis, dashboards, data mining, data integration, and a BI platform that have made it the world's most popular open source BI suite.
Experimental results presented in the book show that data mining is an effective approach for discovering useful rules.