A parameter-free method for discovering generalized clusters in a network

Hiroshi Hirai, Bin Hui Chou, Einoshin Suzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

We show that an MDL-based graph clustering method may be used for discovering generalized clusters from a graph and then extend it so that the input is a network. We define intuitively that generalized clusters contain at least a cluster in which nodes are connected sparsely and the cluster is connected either densely to another cluster or sparsely to another conventional cluster. The first characteristic of the MDL-based graph clustering is a direct outcome of an entropy function used in measuring the encoding length of clusters and the second one is realized through our new encoding method. Experiments using synthetic and real data sets give promising results.

Original languageEnglish
Title of host publicationDiscovery Science - 14th International Conference, DS 2011, Proceedings
Pages135-149
Number of pages15
DOIs
Publication statusPublished - Oct 17 2011
Event14th International Conference on Discovery Science, DS 2011, Co-located with the 22nd International Conference on Algorithmic Learning Theory, ALT 2011 - Espoo, Finland
Duration: Oct 5 2011Oct 7 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6926 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th International Conference on Discovery Science, DS 2011, Co-located with the 22nd International Conference on Algorithmic Learning Theory, ALT 2011
CountryFinland
CityEspoo
Period10/5/1110/7/11

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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    Hirai, H., Chou, B. H., & Suzuki, E. (2011). A parameter-free method for discovering generalized clusters in a network. In Discovery Science - 14th International Conference, DS 2011, Proceedings (pp. 135-149). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6926 LNAI). https://doi.org/10.1007/978-3-642-24477-3_13