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A Novel Research on Rough Clustering Algorithm

Author

Listed:
  • Tao Qu
  • Jinyu Lu
  • Hamid Reza Karimi
  • E Xu

Abstract

The aim of this study is focusing the issue of traditional clustering algorithm subjects to data space distribution influence, a novel clustering algortihm combined with rough set theory is employed to the normal clustering. The proposed rough clustering algorithm takes the condition attributes and decision attributes displayed in the information table as the consistency principle, meanwhile it takes the data supercubic and information entropy to realize data attribute shortcutting and discretizing. Based on above discussion, by applying assemble feature vector addition principle computiation only one scanning information table can realize clustering for the data subject. Experiments reveal that the proposed algorithm is efficient and feasible.

Suggested Citation

  • Tao Qu & Jinyu Lu & Hamid Reza Karimi & E Xu, 2014. "A Novel Research on Rough Clustering Algorithm," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnlaaa:v:2014:y:2014:i:1:n:205062
    DOI: 10.1155/2014/205062
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    References listed on IDEAS

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    1. Shen Yin & Steven Ding & Adel Abandan Sari & Haiyang Hao, 2013. "Data-driven monitoring for stochastic systems and its application on batch process," International Journal of Systems Science, Taylor & Francis Journals, vol. 44(7), pages 1366-1376.
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