A novel fuzzy kernel C-means algorithm for document clustering

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.