Abstract
This paper discusses kernelized functions used in support vector machines defined on fuzzy neighborhoods of a text sequence. We show that a family of functions for fuzzy neighborhoods which are used for text mining or Web information analysis define positive definite kernels under a certain sufficient condition. Accordingly, clustering algorithms using a kernel function, such as agglomerative hierarchical clustering, kernel hard c-means or kernel fuzzy c-means are proposed. Effectiveness of this method is investigated using artificially generated data, and moreover this method is applied to real data.
