A scalable algorithm for high-quality clustering of web snippets

We consider the problem of partitioning, in a highly accurate and highly efficient way, a set of n documents lying in a metric space into k non-overlapping clusters. We augment the well-known furthest-point-first algorithm for k-center clustering in metric spaces with a filtering scheme based on the triangular inequality. We apply this algorithm to Web snippet clustering, comparing it against strong baselines consisting of recent, fast variants of the classical k-means iterative algorithm. Our main conclusion is that our method attains solutions of better or comparable accuracy, and does this within a fraction of the time required by the baselines. Our algorithm is thus valuable when, as in Web snippet clustering, either the real-time nature of the task or the large amount of data make the poorly scalable, traditional clustering methods unsuitable. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: Clustering Keywords Meta Search Engines, Web Snippets, Clus...

• Type: Conference
• Year: 2006
• Where: SAC
• Authors: Filippo Geraci, Marco Pellegrini, Paolo Pisati, Fabrizio Sebastiani
• Keywords: Applied Computing, Metric Spaces, SAC 2006, Web Snippets, Well-known Furthest-point-first Algorithm