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Min Min

Bio: Min Min is an academic researcher from College of Information Technology. The author has contributed to research in topics: Canopy clustering algorithm & k-medians clustering. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

Papers
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Journal ArticleDOI
TL;DR: The result proves that the combined fuzzy clustering based on F-statistic is more effective, which will automatically generate a reasonable clustering numbers and initial cluster center.
Abstract: On analyzing the common problems in fuzzy clustering algorithms, we put forward the combined fuzzy clustering one, which will automatically generate a reasonable clustering numbers and initial cluster center. This clustering algorithm has been tested by real evaluation data of teaching designs. The result proves that the combined fuzzy clustering based on F-statistic is more effective.

3 citations


Cited by
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Journal ArticleDOI
TL;DR: It is demonstrated that the use of membership degrees for fuzzy clustering algorithms - although it is not necessary from the theoretical point of view - is essential for these algorithms to function in practice.

88 citations

Journal Article
Liu Shuai1
TL;DR: A new FCM clustering algorithm with two stages that work out the problem of long time when clustering the large volume of data, achieving better clustering effect and the affectivity and the superiority of the new algorithm are proposed.
Abstract: This paper proposes a new FCM clustering algorithm with two stages,on one hand,the proposed algorithm resolves the problem of the initial choice of the cluster center effectively; and on the other hand,it work out the problem of long time when clustering the large volume of data,achieving better clustering effect. The computer simulated results show the affectivity and the superiority of the new algorithm.

2 citations

Journal Article
TL;DR: A new method of calcuating the transimission closure of a fuzzy similar matrix is obtained by using the degree of fuzzy similarity and graph theory.
Abstract: A new method of calcuating the transimission closure of a fuzzy similar matrix is obtained by using the degree of fuzzy similarity and graph theory. Based on it a new method of fuzzy cluster analysis is proposed.

1 citations