Iterative subgraph mining for principal component analysis

Hiroto Saigo, Koji Tsuda

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

7 Citations (Scopus)

Abstract

Graph mining methods enumerate frequent subgraphs efficiently, but, it is often problematic to summarize the large number of obtained patterns. Thus it makes sense to combine frequent graph mining with principal component analysis to reduce dimensionality and collect a smaller number of characteristic patterns. We present a novel iterative mining algorithm that captures informative patterns corresponding to major entries of top principal components. It repeatedly calls weighted substructure mining where example weights are updated in each iteration. The Lanczos algorithm, a standard algorithm of eigendecomposition, is employed to update the weights. In experiments, our patterns are shown to approximate the principal components obtained by frequent mining.

Original languageEnglish
Title of host publicationProceedings - 8th IEEE International Conference on Data Mining, ICDM 2008
Pages1007-1012
Number of pages6
DOIs
Publication statusPublished - Dec 1 2008
Externally publishedYes
Event8th IEEE International Conference on Data Mining, ICDM 2008 - Pisa, Italy
Duration: Dec 15 2008Dec 19 2008

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Other

Other8th IEEE International Conference on Data Mining, ICDM 2008
CountryItaly
CityPisa
Period12/15/0812/19/08

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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