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Data Analysis and Knowledge Discovery  2018, Vol. 2 Issue (7): 81-88    DOI: 10.11925/infotech.2096-3467.2017.1333
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A Fuzzy C-Means Algorithm Based on Huffman Tree
Mansheng Xiao,Lijuan Zhou(),Zhicheng Wen
School of Computer Science, Hunan University of Technology, Zhuzhou 412007, China
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Abstract  

[Objective] This paper tries to solve the issues facing traditional FCM algorithm, such as randomly choosing initial cluster center, sensitive to noise, and only capable of clustering the equally distributed samples. [Methods] We proposed a new FCM clustering algorithm based on Huffman tree with dissimilarity degree matrix of high density sample sets. The new algorithm could get initial clustering centers, and then generate the membership function of the non-normalized constraint samples. [Results] We examined the proposed algorithm with man-made samples, images, and UCI datasets. The clustering accuracy and the computation time of the new algorithm were better than algorithms based on the Gauss kernel or traditional FCM. [Limitations] The β of the sample density adjustment factor was decided by experiment or experience without theoretical supports. [Conclusions] The proposed algorithm could be used for clustering data sets with high level of noise and distributed unequally.

Key wordsDensity of Samples      Dissimilarity Degree      Huffman Tree      Membership     
Received: 28 December 2017      Published: 15 August 2018

Cite this article:

Mansheng Xiao, Lijuan Zhou, Zhicheng Wen. A Fuzzy C-Means Algorithm Based on Huffman Tree. Data Analysis and Knowledge Discovery, 2018, 2(7): 81-88.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.1333     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2018/V2/I7/81

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