Fast Fuzzy Clustering of Web Page Collections
Christian Borgelt, Andreas Nürnberger · 2004
Abstract. We study an extension of learning vector quantization that draws on ideas from fuzzy clustering, enabling us to find fuzzy clusters of ellipsoidal shape with a competitive learning scheme. This approach may be seen as a kind of online fuzzy clustering, which can have advantages w.r.t. the execution time of the clustering algorithm. We demonstrate the usefulness of our approach by applying it to web page collections, which are, in general, difficult to cluster due to the high number of dimensions and the special distribution characteristics of the data. 1