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Recent Readings

Book review: Darwinia
Reviewed: Friday, August 11, 2006

Summer reading: Spin
Reviewed: Saturday, August 5, 2006

Reviewed: Tuesday, July 18, 2006

the Omnivoire's Delimma
Reviewed: Wednesday, July 12, 2006

the Golem's Eye
Reviewed: Wednesday, May 31, 2006


Clustering Relational Data Using Attribute and Link Information
David Jensen, Micah Adler and Jennifer Neville, 2003 , (Paper URL)
Thursday, April 29, 2004

This paper by David Jensen, Micah Adler and Jennifer Neville describes a method to use both link and attribute information to cluster graphs of relational data. It was presented at a Text Mining and Link Analysis Workshop as part of IJCAI-2003. The method combines link weights with attribute weights to form a similarity metric and then uses one of three graph partitioning algorithms (min-cut, majorclust and Spectral) to partition the graph. All three methods rely on the assumption that linkages are dependent on similar attribute values (which is reasonable in many domains). It was tested with synthetic data using various levels of independence. All of the methods work well when the link and attribute data are highly correlated. Spectral seems to work best even in the presence of more noise. All in all, the paper covers a reasonable idea reasonably well.

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Copyright -- Gary Warren King, 2004 - 2006