Volume 8 Number 1 (Jan. 2013)
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JSW 2013 Vol.8(1): 134-141 ISSN: 1796-217X
doi: 10.4304/jsw.8.1.134-141

Study of Clustering Algorithm based on Fuzzy C-Means and Immunological Partheno Genetic

Hongfen Jiang1, Yijun Liu1, Feiyue Ye1, Haixu Xi1, Mingfang Zhu1, Junfeng Gu2
1College of Computer Engineering, Jiangsu Teachers University of Technology, Changzhou 213001, China
2College of Petroleum Engineering, Changzhou University, Changzhou 213001, China

Abstract—Clustering algorithm is very important for data mining. Fuzzy c-means clustering algorithm is one of the earliest goal-function clustering algorithms, which has achieved much attention. This paper analyzes the lack of fuzzy C-means (FCM) algorithm and genetic clustering algorithm. Propose a hybrid clustering algorithm based on immune single genetic and fuzzy C-means. This algorithm uses the fuzzy clustering of Immune Partheno-Genetic to guide the number and the choice of the clustering centers. And then utilize FCM to make the clustering (IPGA-FCM). This algorithm not only overcomes the local optimal problem of FCM, the choice of the initial value is inappropriate, but also overcomes the contradictions between the search speed and clustering accuracy of the general genetic clustering algorithm. Then it applies the novel clustering algorithm to Chinese document clustering. The clustering algorithm is superior to other ordinary clustering algorithm and the result can embody the wide diversity and large amount of Chinese document. Experiments show the algorithm is effective.

Index Terms—Clustering analysis, genetic algorithm, FCM, Immune mechanism, Text clustering.


Cite: Hongfen Jiang, Yijun Liu, Feiyue Ye,Haixu Xi, Mingfang Zhu, Junfeng Gu, "Study of Clustering Algorithm based on Fuzzy C-Means and Immunological Partheno Genetic," Journal of Software vol. 8, no. 1, pp. 134-141, 2013.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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