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 ?
The unique hierarchical features of educational data (Baker, 2011) provide researchers in educational environment with opportunities to use data mining for investigation.
Learning Analytics is data analytics in the context of learning and education; that is, the collection of data about learners' activities and behaviour as well as data about the environment and context in which the learning took place; and the analysis of such data using statistics and data mining techniques with the purpose of extracting relevant patterns from this data to better understand the learning that took place.
Changing the results of Data Mining Applications to preserve privacy: The privacy of the data can be compromised by the results of data mining applications such as association rule or classification rule mining.
Data mining has played a key role in assisting the difficult job of improving this quality [4] have applied the data mining techniques in their research to improve the quality of education.
Some of these elements were not used in data mining techniques because they are not related to input/output operations.
Although in video preprocessing, video data streams have been divided based on shot or object, we usually have to extract some important video features and then implement data mining. Video feature extraction and video data mining are not separable.
Graphet Data Mining transforms customers' complex energy data into fact-based energy management strategies for sustainable energy conservation and cost savings.
The problem of secured data mining has found considerable attention in recent years because of the recent concerns on the privacy of underlying data [3].
Often there is a temptation to just dive in and start reviewing individual documents, rather than spending time on the broad brush analysis which data mining allows.
Recently, the concept of educational data mining (EDM) has witnessed dramatic worldwide growth in the field of education.
Data mining approaches are most effective in helping us extract the insights into customer behaviour, habits, potential needs and desires, credit associated risks, fraudulent transactions etc.
Unfortunately, in many firms data mining has not lived up to the hype and potential.