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.
McGraw-Hill Dictionary of Scientific & Technical Terms, 6E, Copyright © 2003 by The McGraw-Hill Companies, Inc.

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.

McGraw-Hill Concise Encyclopedia of Engineering. © 2002 by The McGraw-Hill Companies, Inc.

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.
This article is provided by FOLDOC - Free Online Dictionary of Computing (

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 social media mining, 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.)
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References in periodicals archive ?
1) which helps us with the pre-processing phase of web usage mining and with preparation of the log file for another phase.
E-commerce means more than just build up a web site, then sit back and relax.Web Mining systems need to be implemented to Understand visitors' profiles, Identify company's strengths and weaknesses and Measure the effectiveness of online marketing efforts.Web Mining support on-going, continuous improvements for Ebusinesses.Web Usage Mining techniques are used to find out hidden patterns to improve business ideas.
Rukmani, "Implementation of web usage mining using APRIORI and FP growth algorithms," International Journal ofAdvanced Networking and Applications, vol.
This statistic is a non parametric test suitable to distributions that are not normal such as the exponential distributions observed in web usage mining or web log analysis [15].
Jayakarthik, Improvement of the non functional requirement using the web personalization with the adaptation of web usage mining, International Journal of Computing Technology, Vol II, No 4, March 2012.
[1.] Ba-Omar, H., Petrounias, I., Anwar, F.: A Framework for Using Web Usage Mining to Personalise E-learning.
Web Usage Mining for a Better Web-based Learning Environment.
Discovering Internet marketing intelligence through online analytical Web usage mining. ACM SIGMOD Record (Dec.
Definition 3 Web usage mining is a process of analyzing information left by user access to the Web server and finding user access patterns (Huml J., Cerkasov J., Margarisova K., et al., 2015).
Other related methods includes cleaning of data for data mining and warehousing [18], duplicate data detection in text databases [12] and preprocessing for usage Mining [16].
How to construct web usage ontology on semantic web becomes a mandatory requirement of web usage mining [1].