Iterative subgraph mining for principal component analysis

Hiroto Saigo, Koji Tsuda

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

7 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルProceedings - 8th IEEE International Conference on Data Mining, ICDM 2008
ページ1007-1012
ページ数6
DOI
出版ステータス出版済み - 12 1 2008
外部発表はい
イベント8th IEEE International Conference on Data Mining, ICDM 2008 - Pisa, イタリア
継続期間: 12 15 200812 19 2008

出版物シリーズ

名前Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN(印刷版)1550-4786

その他

その他8th IEEE International Conference on Data Mining, ICDM 2008
国/地域イタリア
CityPisa
Period12/15/0812/19/08

All Science Journal Classification (ASJC) codes

  • 工学(全般)

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