A data mining approach to database compression

Chin-Feng Lee (), S. Wesley Changchien (), Wei-Tse Wang () and Jau-Ji Shen ()
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Chin-Feng Lee: Chaoyang University of Technology
S. Wesley Changchien: National Chung-Hsing University
Wei-Tse Wang: Chaoyang University of Technology
Jau-Ji Shen: National Chung Hsing University

Information Systems Frontiers, 2006, vol. 8, issue 3, No 1, 147-161

Abstract: Abstract Data mining can dig out valuable information from databases to assist a business in approaching knowledge discovery and improving business intelligence. Database stores large structured data. The amount of data increases due to the advanced database technology and extensive use of information systems. Despite the price drop of storage devices, it is still important to develop efficient techniques for database compression. This paper develops a database compression method by eliminating redundant data, which often exist in transaction database. The proposed approach uses a data mining structure to extract association rules from a database. Redundant data will then be replaced by means of compression rules. A heuristic method is designed to resolve the conflicts of the compression rules. To prove its efficiency and effectiveness, the proposed approach is compared with two other database compression methods.

Keywords: Database compression; Data mining; Association rules (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10796-006-8777-x

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