Standardized Evaluation Method for Web Clustering Results

Authors:
Daniel Crabtree;Xiaoying Gao;Peter Andreae
Affiliations:
Victoria University of Wellington;Victoria University of Wellington;Victoria University of Wellington
Venue:
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Year:
2005

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Abstract

Web clustering assists users of a search engine by presenting search results as clusters of related pages. Many clustering algorithms with different characteristics have been developed: but the lack of a standardized web clustering evaluation method that can evaluate clusterings with different characteristics has prevented effective comparison of algorithms. The paper solves this by introducing a new structure for defining general ideal clusterings and new measurements for evaluating clusterings with different characteristics by comparing them against the general ideal clustering.