Noodles: A Clustering Engine for the Web

  • Giansalvatore Mecca
  • Salvatore Raunich
  • Alessandro Pappalardo
  • Donatello Santoro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4607)


The paper describes the Noodles system, a clustering engine for Web and desktop searches. By employing a new algorithm for document clustering, based on Latent Semantic Indexing, Noodles provides good classification power to simplify browsing of search results by casual users. In the paper, we provide some background about the problem of clustering search results, give an overview of the novel techniques implemented in the system, and present its architecture and main features.


Document Cluster Latent Semantic Indexing Local Disk Document Space Desktop User Interface 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Chekuri, C., Raghavan, P.: Web Search Using Automatic Classification. In: Proceedings of the World Wide Web Conference (1997)Google Scholar
  2. 2.
    Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by Latent Semantic Analysis. Journal of the American Society for Information Sciences 41(6), 391–407 (1990)CrossRefGoogle Scholar
  3. 3.
    Guha, R., Mc Cool, R., Miller, E.: Semantic Search. In: Proceedings of the World Wide Web Conference (2003)Google Scholar
  4. 4.
    Mecca, G., Raunich, S., Pappalardo, A.: A New Algorithm to Cluster Search Results. Data and Knowledge Engineering, To appear:Available as Noodles WR–01–2006 at (2007)
  5. 5.
    Osinski, S., Stefanowski, J., Weiss, D.: Lingo: Search Results Clustering Algorithm Based on Singular Value Decomposition. In: Proceedings of the International Conference on Intelligent Information Systems (IIPWM) (2004)Google Scholar
  6. 6.
    Osinski, S., Weiss, D.: A Concept-Driven Algorithm for Clustering Search Results. IEEE Intelligent Systems 20(3), 48–54 (2005)CrossRefGoogle Scholar
  7. 7.
    The Grokker Search Engine:
  8. 8.
    The SRC Search Engine:
  9. 9.
    The Vivisimo Search Engine:
  10. 10.
    van Rijsbergen, C.J.: Information Retrieval (2nd edn.) London, Butterworths (1979)Google Scholar
  11. 11.
    Web 2.0 – Exclusive Demonstration of Clustering from Google.
  12. 12.
    Zamir, O., Etzioni, O.: Web Document Clustering: A Feasibility Demonstration. In: Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (1998)Google Scholar
  13. 13.
    Zamir, O., Etzioni, O.: Grouper: A Dynamic Clustering Interface for Web Search Results. Computer Networks 31(11-16), 1361–1374 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Giansalvatore Mecca
    • 1
  • Salvatore Raunich
    • 1
  • Alessandro Pappalardo
    • 1
  • Donatello Santoro
    • 1
  1. 1.Dipartimento di Matematica e Informatica, Università della Basilicata, PotenzaItaly

Personalised recommendations