Multiple kernel approach to semi-supervised fuzzy clustering algorithm for land-cover classification. (February 2018)

• Record Type: Journal Article
• Authors: Mai, Sinh Dinh; Ngo, Long Thanh
• Is Part Of: Engineering applications of artificial intelligence. Volume 68(2017:Aug.)
• Page Start: 205
• Page End: 213
• Publication Date: 2018-02
• Subjects: Semi-supervised clustering -- Fuzzy clustering -- Kernel fuzzy c-means -- Multiple kernel -- Land cover classification; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2017.11.007
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Clustering is used to detect sound structures or patterns in a dataset in which objects positioned within the same cluster exhibit a substantial level of similarity. In numerous clustering problems, patterns is not easily separable due to the highly complex shaped data. In the previous studies, kernel-based methods have exhibited the effectiveness to partition such data. In this paper, we proposed a semi-supervised clustering method based fuzzy c-means algorithm using multiple kernel technique, called SMKFCM, in which the rudimentary centroids are directly used to the calculating process of centroids. The SMKFCM algorithm is on the basis of combining the labeled and unlabeled data together to improve performance. We used the labeled patterns to calculate the centrality of clusters considered as the rudimentary centroids which are added into the objective functions. The SMKFCM algorithm can be applied to both clustering and classification problems. The experimental results show that SMKFCM algorithm can improve significantly the classification accuracy which comes from comparison with a conventional classification or clustering algorithms such as semi-supervised kernel fuzzy c-means (S2KFCM), semi-supervised fuzzy c-means (SFCM) and Self-trained semi-supervised SVM algorithm (PS3VM).