A review of data mining techniques

Sang Jun Lee; Keng Siau

doi:10.1108/02635570110365989

A review of data mining techniques

Sang Jun Lee, Keng Siau

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

179 Citations (Scopus)

Abstract

Terabytes of data are generated everyday in many organizations. To extract hidden predictive information from large volumes of data, data mining (DM) techniques are needed. Organizations are starting to realize the importance of data mining in their strategic planning and successful application of DM techniques can be an enormous payoff for the organizations. This paper discusses the requirements and challenges of DM, and describes major DM techniques such as statistics, artificial intelligence, decision tree approach, genetic algorithm, and visualization.

Original language	English
Pages (from-to)	41-46
Journal	Industrial Management and Data Systems
Volume	101
Issue number	1
DOIs	https://doi.org/10.1108/02635570110365989
Publication status	Published - 2001
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

Algorithms
Artificial intelligence
Data mining
Decision trees

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abstract = "Terabytes of data are generated everyday in many organizations. To extract hidden predictive information from large volumes of data, data mining (DM) techniques are needed. Organizations are starting to realize the importance of data mining in their strategic planning and successful application of DM techniques can be an enormous payoff for the organizations. This paper discusses the requirements and challenges of DM, and describes major DM techniques such as statistics, artificial intelligence, decision tree approach, genetic algorithm, and visualization.",

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AU - Siau, Keng

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PY - 2001

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N2 - Terabytes of data are generated everyday in many organizations. To extract hidden predictive information from large volumes of data, data mining (DM) techniques are needed. Organizations are starting to realize the importance of data mining in their strategic planning and successful application of DM techniques can be an enormous payoff for the organizations. This paper discusses the requirements and challenges of DM, and describes major DM techniques such as statistics, artificial intelligence, decision tree approach, genetic algorithm, and visualization.

AB - Terabytes of data are generated everyday in many organizations. To extract hidden predictive information from large volumes of data, data mining (DM) techniques are needed. Organizations are starting to realize the importance of data mining in their strategic planning and successful application of DM techniques can be an enormous payoff for the organizations. This paper discusses the requirements and challenges of DM, and describes major DM techniques such as statistics, artificial intelligence, decision tree approach, genetic algorithm, and visualization.

KW - Algorithms

KW - Artificial intelligence

KW - Data mining

KW - Decision trees

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DO - 10.1108/02635570110365989

M3 - RGC 21 - Publication in refereed journal

SN - 0263-5577

VL - 101

SP - 41

EP - 46

JO - Industrial Management and Data Systems

JF - Industrial Management and Data Systems

IS - 1

ER -

A review of data mining techniques

Abstract

Bibliographical note

Research Keywords

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