Cluster Generation and Cluster Labelling for Web Snippets: A Fast and Accurate Hierarchical Solution

This paper describes Armil, a meta-search engine that groups the Web snippets returned by auxiliary search engines into disjoint labelled clusters. The cluster labels generated by Armil provide the user with a compact guide to assessing the relevance of each cluster to his/her information need. Striking the right balance between running time and cluster well-formedness was a key point in the design of our system. Both the clustering and the labelling tasks are performed on the fly by processing only the snippets provided by the auxiliary search engines, and use no external sources of knowledge. Clustering is performed by means of a fast version of the furthest-point-first algorithm for metric k-center clustering. Cluster labelling is achieved by combining intracluster and inter-cluster term extraction based on a variant of the information gain measure. We have tested the clustering effectiveness of Armil against Vivisimo, the de facto industrial standard in Web snippet clustering, using as benchmark a comprehensive set of snippets obtained from the Open Directory Project hierarchy. According to two widely accepted “external” metrics of clustering quality, Armil achieves better performance levels by 10%. We also report the results of a thorough user evaluation of both the clustering and the cluster labelling algorithms. On a standard desktop PC (AMD Athlon 1Ghz Clock with 750 Mbytes RAM), Armil performs clustering and labelling altogether of up to 200 snippets in less than one second. 1A Preliminary version of this work has appeared in the proceedings of ACM SAC 2006 [10] and SPIRE 2006 [11]. Work partially supported by the Italian Registry for the ccTLD“.it”.