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A data mining approach to database compression

Author

Listed:
  • 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)

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.

Suggested Citation

  • Chin-Feng Lee & S. Wesley Changchien & Wei-Tse Wang & Jau-Ji Shen, 2006. "A data mining approach to database compression," Information Systems Frontiers, Springer, vol. 8(3), pages 147-161, July.
    • Handle: RePEc:spr:infosf:v:8:y:2006:i:3:d:10.1007_s10796-006-8777-x
    DOI: 10.1007/s10796-006-8777-x
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    Citations

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    Cited by:

    1. Chulhwan Chris Bang, 2015. "Information systems frontiers: Keyword analysis and classification," Information Systems Frontiers, Springer, vol. 17(1), pages 217-237, February.
    2. Vincent S. Tseng & Hsieh-Hui Yu & Shih-Chiang Yang, 2009. "Efficient mining of multilevel gene association rules from microarray and gene ontology," Information Systems Frontiers, Springer, vol. 11(4), pages 433-447, September.
    3. Guanling Lee & Sheng-Lung Peng & Yuh-Tzu Lin, 2009. "Proportional fault-tolerant data mining with applications to bioinformatics," Information Systems Frontiers, Springer, vol. 11(4), pages 461-469, September.

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