Journal
IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume 27, Issue 1, Pages 31-44Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TFUZZ.2018.2856081
Keywords
Clustering structure; fuzzy similarity matrix; joint learning; neighbors of data points; spectral clustering
Funding
- Japan Society for the Promotion of Science (JSPS)
- National Natural Science Foundation of China (NSFC) [61272210, 61572236, 61300151]
- NSFC-JSPS [6161101250]
- Natural Science Foundation of Jiangsu Province [BK20161268]
- National first-class discipline program of light industry technology and engineering (LITE2018)
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When spectral clustering analysis is applied, a similarity matrix of data plays a vital role in both clustering performance and stability of clustering results. In order to enhance the clustering performance and maintain the stability of the clustering results, a new method to jointly learn the similarity matrix and the clustering structure, called the joint learning method (FSCM) of spectral clustering structure and fuzzy similarity matrix of data, is proposed in this paper. In FSCM, the capability of a double-index fuzzy C-means clustering algorithm is used to determine an appropriate fuzzy similarity between any pair of data points. A fuzzy similarity matrix of data is also determined by adaptively assigning fuzzy neighbors of data points so the spectral clustering structure of data can he found and the clustering stability of FSCM can be assured. Experimental results on synthetic and real datasets demonstrate the effectiveness of the proposed method.
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