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Abstract and Applied Analysis - Volume 2014 2014, Article ID 205062, 6 pages -

Research Article

School of Electronics & Information Engineering, University of Technology Liaoning, Jinzhou 121013, China

College of Engineering, Bohai University, Jinzhou 121013, China

Department of Engineering, Faculty of Engineering and Science, The University of Agder, 4898 Grimstad, Norway

Received 25 January 2014; Accepted 7 February 2014; Published 12 March 2014

Academic Editor: Shen Yin

Copyright © 2014 Tao Qu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.





Author: Tao Qu, Jinyu Lu, Hamid Reza Karimi, and E Xu

Source: https://www.hindawi.com/



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