Spectral co-clustering for dynamic bipartite graphs

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Title: Spectral co-clustering for dynamic bipartite graphs
Authors: Greene, Derek
Cunningham, Pádraig
Permanent link: http://hdl.handle.net/10197/2588
Date: 24-Sep-2010
Abstract: A common task in many domains with a temporal aspect involves identifying and tracking clusters over time. Often dynamic data will have a feature-based representation. In some cases, a direct mapping will exist for both objects and features over time. But in many scenarios, smaller subsets of objects or features alone will persist across successive time periods. To address this issue, we propose a dynamic spectral co-clustering method for simultaneously clustering objects and features over time, as represented by successive bipartite graphs. We evaluate the method on a benchmark text corpus and Web 2.0 tagging data.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: Sun SITE Central Europe (CEUR)
Copyright (published version): 2010 for the individual papers by the papers' authors
Keywords: Machine learningClustering analysisText mining
Subject LCSH: Machine learning
Cluster analysis
Bipartite graphs
Other versions: http://ceur-ws.org/Vol-655/dynak2010_paper3.pdf
Language: en
Status of Item: Peer reviewed
Is part of: Pensa, R.G. et al (eds.). DyNaK 2010 : Proceedings of the 1st Workshop on Dynamic Networks and Knowledge Discovery Barcelona, Spain, September 24, 2010, CEUR Workshop Proceedings, Vol. 655
Conference Details: Paper presented at the Workshop on Dynamic Networks and Knowledge Discovery (DyNAK 2010) at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2010), Barcelona, September 24th 2010
Appears in Collections:Computer Science Research Collection

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