@inproceedings{Lu:2009:ETN:1645953.1646167,
  abstract = {In this poster, we investigate how to enhance web clustering by leveraging the tripartite network of social tagging systems. We propose a clustering method, called "Tripartite Clustering", which cluster the three types of nodes (resources, users and tags) simultaneously based on the links in the social tagging network. The proposed method is experimented on a real-world social tagging dataset sampled from del.icio.us. We also compare the proposed clustering approach with K-means. All the clustering results are evaluated against a human-maintained web directory. The experimental results show that Tripartite Clustering significantly outperforms the content-based K-means approach and achieves performance close to that of social annotation-based K-means whereas generating much more useful information.},
  acmid = {1646167},
  address = {New York, NY, USA},
  author = {Lu, Caimei and Chen, Xin and Park, E. K.},
  booktitle = {Proceeding of the 18th ACM conference on Information and knowledge management},
  doi = {10.1145/1645953.1646167},
  interhash = {e192e53972f28d78f1ecbffbfea08bed},
  intrahash = {86160cf68758ec60922323a34a7833f0},
  isbn = {978-1-60558-512-3},
  location = {Hong Kong, China},
  numpages = {4},
  pages = {1545--1548},
  publisher = {ACM},
  series = {CIKM '09},
  title = {Exploit the tripartite network of social tagging for web clustering},
  url = {http://doi.acm.org/10.1145/1645953.1646167},
  year = 2009
}

@article{Carpineto:2009:SWC:1541880.1541884,
  abstract = {Web clustering engines organize search results by topic, thus offering a complementary view to the flat-ranked list returned by conventional search engines. In this survey, we discuss the issues that must be addressed in the development of a Web clustering engine, including acquisition and preprocessing of search results, their clustering and visualization. Search results clustering, the core of the system, has specific requirements that cannot be addressed by classical clustering algorithms. We emphasize the role played by the quality of the cluster labels as opposed to optimizing only the clustering structure. We highlight the main characteristics of a number of existing Web clustering engines and also discuss how to evaluate their retrieval performance. Some directions for future research are finally presented.},
  acmid = {1541884},
  address = {New York, NY, USA},
  articleno = {17},
  author = {Carpineto, Claudio and Osi\'{n}ski, Stanislaw and Romano, Giovanni and Weiss, Dawid},
  doi = {10.1145/1541880.1541884},
  interhash = {95beef372c0d7c6f57caf0862896a0bb},
  intrahash = {1921bab51019d89a0b740c43d8aafd23},
  issn = {0360-0300},
  issue = {3},
  issue_date = {July 2009},
  journal = {ACM Comput. Surv.},
  month = {July},
  numpages = {38},
  pages = {17:1--17:38},
  publisher = {ACM},
  title = {A survey of Web clustering engines},
  url = {http://doi.acm.org/10.1145/1541880.1541884},
  volume = 41,
  year = 2009
}