A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters
Joint Authors
Ren, Min
Liu, Peiyu
Wang, Zhihao
Yi, Jing
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-11-29
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters.
Firstly, a density-based algorithm was put forward.
The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule n and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum.
Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function.
Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process.
At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result.
American Psychological Association (APA)
Ren, Min& Liu, Peiyu& Wang, Zhihao& Yi, Jing. 2016. A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1099605
Modern Language Association (MLA)
Ren, Min…[et al.]. A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-12.
https://search.emarefa.net/detail/BIM-1099605
American Medical Association (AMA)
Ren, Min& Liu, Peiyu& Wang, Zhihao& Yi, Jing. A Self-Adaptive Fuzzy c-Means Algorithm for Determining the Optimal Number of Clusters. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1099605
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099605