Joint Learning of Spectral Clustering Structure and Fuzzy Similarity Matrix of Data
| Autor: | Hisao Ishibuchi, Zekang Bian, Shitong Wang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Applied Mathematics Similarity matrix Pattern recognition 02 engineering and technology Fuzzy logic Spectral clustering ComputingMethodologies_PATTERNRECOGNITION Data point Computational Theory and Mathematics Artificial Intelligence Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering Entropy (information theory) Learning methods 020201 artificial intelligence & image processing Fuzzy similarity Artificial intelligence Cluster analysis business |
| Zdroj: | IEEE Transactions on Fuzzy Systems. 27:31-44 |
| ISSN: | 1941-0034 1063-6706 |
| DOI: | 10.1109/tfuzz.2018.2856081 |
| Popis: | 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 be found and the clustering stability of FSCM can be assured. Experimental results on synthetic and real datasets demonstrate the effectiveness of the proposed method. |
| Databáze: | OpenAIRE |
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