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.

@INPROCEEDINGS{SEM05,
    AUTHOR =    {Daniel Crabtree and Xiaoying Gao and Peter Andreae},
    TITLE =     {Standardized Evaluation Method for Web Clustering Results},
    BOOKTITLE = {The 2005 IEEE/WIC/ACM International Conference 
                 on Web Intelligence (WI'05)},
    YEAR =      {2005},
    PAGES =     {280--283}
}