A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fuzzy C-Means Clustering Algorithm Based on Kernel Method
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Data Clustering with Partial Supervision
Data Mining and Knowledge Discovery
Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning (Studies in Computational Intelligence)
Semi-Supervised Learning
KFCSA: a novel clustering algorithm for high-dimension data
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
Validity-guided (re)clustering with applications to image segmentation
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
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Fuzzy Kernel C-Means (FKCM) algorithm can improve accuracy significantly compared with classical Fuzzy C-Means algorithms for nonlinear separability, high dimension and clusters with overlaps in input space. Despite of these advantages, several features are subjected to the applications in real world such as local optimal, outliers, the c parameter must be assigned in advance and slow convergence speed. To overcome these disadvantages, Semi-Supervised learning and validity index are employed. Semi-Supervised learning uses limited labeled data to assistant a bulk of unlabeled data. It makes the FKCM avoid drawbacks proposed. The number of cluster will great affect clustering performance. It isn't possible to assume the optimal number of clusters especially to large text corps. Validity function makes it possible to determine the suitable number of cluster in clustering process. Sparse format, scatter and gathering strategy save considerable store space and computation time. Experimental results on the Reuters-21578 benchmark dataset demonstrate that the algorithm proposed is more flexibility and accuracy than the state-of-art FKCM.