Spectral co-clustering for dynamic bipartite graphs
|Title:||Spectral co-clustering for dynamic bipartite graphs||Authors:||Greene, Derek
|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 learning; Clustering analysis; Text mining||Subject LCSH:||Machine learning
|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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