A data mining approach to database compression

Authors:
Chin-Feng Lee;S. Wesley Changchien;Wei-Tse Wang;Jau-Ji Shen
Affiliations:
Department of Information Management, Chaoyang University of Technology, Taichung County, R.O.C. 41349;Institute of Electronic Commerce, National Chung-Hsing University, Taichung, ROC 402;Department of Information Management, Chaoyang University of Technology, Taichung County, R.O.C. 41349;Department of Information Management, National Chung Hsing University, Taichung, R.O.C. 402
Venue:
Information Systems Frontiers
Year:
2006

Citing 0
Cited 2

Proportional fault-tolerant data mining with applications to bioinformatics

Information Systems Frontiers
Efficient mining of multilevel gene association rules from microarray and gene ontology

Information Systems Frontiers

Quantified Score

Hi-index	0.00

Visualization

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.