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- Title
Improved collaborative clustering algorithm based on entropy weight.
- Authors
GAO Cui-fang; HUANG Shan-wei; SHEN Wan-qiang; YIN Ping
- Abstract
In order to overcome the disadvantage of large amount of calculation in collaborative clustering, this paper proposed an entropy-weighted fuzzy collaborative clustering algorithm. First, it introduced the entropy to measure the uncertain information in the difference matrices of membership degree. Second, it defined the entropy-weighted distance for similarity according to the available information. Last, it offered and implemented the collaborative clustering by means of the contribution of the weighted index. The experimental results show that the improved algorithm can take full advantage of the relevant information among the subsets and achieve more accurate collaborative results with high computational efficiency. Compared with the exis-ting clustering, the improved algorithm can automatically calculate the collaborative relationship strength, so it can simplify the assignment of parameters and the calculation of collaborative function.
- Publication
Application Research of Computers / Jisuanji Yingyong Yanjiu, 2015, Vol 32, Issue 4, p1016
- ISSN
1001-3695
- Publication type
Article
- DOI
10.3969/j.issn.1001-3695.2015.04.013