Data mining: An overview from a database perspective

Ming Syan Chen, Jiawei Han, Philip S. Yu

Research output: Contribution to journalReview articlepeer-review

Abstract

Mining information and knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. Researchers in many different fields have shown great interest in data mining. Several emerging applications in information providing services, such as data warehousing and on-line services over the Internet, also call for various data mining techniques to better understand user behavior, to improve the service provided, and to increase the business opportunities. In response to such a demand, this article is to provide a survey, from a database researcher's point of view, on the data mining techniques developed recently. A classification of the available data mining techniques is provided, and a comparative study of such techniques is presented.

Original languageEnglish (US)
Pages (from-to)866-883
Number of pages18
JournalIEEE Transactions on Knowledge and Data Engineering
Volume8
Issue number6
DOIs
StatePublished - 1996
Externally publishedYes

Keywords

  • Association rules
  • Classification
  • Data clustering
  • Data cubes
  • Data generalization and characterization
  • Data mining
  • Knowledge discovery
  • Multiple-dimensional databases
  • Pattern matching algorithms

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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