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

Hiroshi Hirai; Bin Hui Chou; Einoshin Suzuki

doi:10.1007/978-3-642-24477-3_13

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

Hiroshi Hirai, Bin Hui Chou, Einoshin Suzuki

Intelligence Science

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Discovery Science - 14th International Conference, DS 2011, Proceedings
Pages	135-149
Number of pages	15
DOIs	https://doi.org/10.1007/978-3-642-24477-3_13
Publication status	Published - 2011
Event	14th 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 2011 → Oct 7 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6926 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	14th International Conference on Discovery Science, DS 2011, Co-located with the 22nd International Conference on Algorithmic Learning Theory, ALT 2011
Country/Territory	Finland
City	Espoo
Period	10/5/11 → 10/7/11

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

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10.1007/978-3-642-24477-3_13

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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

A parameter-free method for discovering generalized clusters in a network. / Hirai, Hiroshi; Chou, Bin Hui; Suzuki, Einoshin.
Discovery Science - 14th International Conference, DS 2011, Proceedings. 2011. p. 135-149 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6926 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hirai, H, Chou, BH & Suzuki, E 2011, A parameter-free method for discovering generalized clusters in a network. in Discovery Science - 14th International Conference, DS 2011, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6926 LNAI, pp. 135-149, 14th International Conference on Discovery Science, DS 2011, Co-located with the 22nd International Conference on Algorithmic Learning Theory, ALT 2011, Espoo, Finland, 10/5/11. https://doi.org/10.1007/978-3-642-24477-3_13

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AB - 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.

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

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