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Non-crisp Clustering Web Visitors by Fast, Convergent and Robust Algorithms on Access Logs

The algorithms proposed to date for categorizing WEB-visitors are all of quadratic time complexity (they essentially require computing the dissimilarity between all pairs of paths). These clustering eeorts, although not scalable, have demonstrated the extensive beneets and sophisticated applications emerging from identifying groups of visitors to a web site. We provide a sub-quadratic clustering algorithms for generic dissimilarity in paths. Our algorithms are robust because they use medians rather than means as estimators of location, and the resulting representative of a cluster is actually a path in the item set. We demonstrate mathematically that our algorithms converge. The methods proposed generalize approaches that allow a data item to have a degree of membership in a cluster. Because our algorithm is generic to both, fuzzy membership approaches and probabilistic approaches for partial membership, we simply name it non-crisp clustering.