PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Geraci F., Maggini M., Pellegrini M., Sebastiani F. Cluster generation and labeling for Web snippets: a fast, accurate hierarchical solution. In: Internet Mathematics, vol. 3 (4) pp. 413 - 443. Special Issue on Algorithmic and Numerical Aspects of Web Search. Gianna Del Corso, Stefano Leonardi (eds.). A K Peters Ltd, 2008.
 
 
Abstract
(English)
This paper describes Armil, a meta-search engine that groups the web snippets returned by auxiliary search engines into disjoint labeled 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 labeling tasks are performed on the fly by processing only the snippets provided by the auxiliary search engines, and they use no external sources of knowledge. Clustering is performed by means of a fast version of the furthest-pointfirst algorithm for metric k-center clustering. Cluster labeling is achieved by combining intra-cluster 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 labeling algorithms. On a standard desktop PC (AMD Athlon 1-Ghz Clock with 750 Mbytes RAM), Armil performs clustering and labeling altogether of up to 200 snippets in less than one second.
URL: http://www.internetmathematics.org/contents.htm
Subject Clustering
Search Engines
Snippet Clustering
Cluster Labelling
I.2.6 Learning
H.3.3 Information Search and Retrieval


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