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Iterative Subgraph Mining for Principal Component Analysis

2008

Conference Paper

ei


Graph mining methods enumerate frequent subgraphs efficiently, but they are not necessarily good features for machine learning due to high correlation among features. Thus it makes sense to perform principal component analysis to reduce the dimensionality and create decorrelated features. 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.

Author(s): Saigo, H. and Tsuda, K.
Book Title: ICDM 2008
Journal: Proceedings of the IEEE International Conference on Data Mining (ICDM 2008)
Pages: 1007-1012
Year: 2008
Month: December
Day: 0
Editors: Giannotti, F. , D. Gunopulos, F. Turini, C. Zaniolo, N. Ramakrishnan, X. Wu
Publisher: IEEE Computer Society

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/ICDM.2008.62
Event Name: IEEE International Conference on Data Mining
Event Place: Pisa, Italy

Address: Los Alamitos, CA, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{5514,
  title = {Iterative Subgraph Mining for Principal Component Analysis},
  author = {Saigo, H. and Tsuda, K.},
  journal = {Proceedings of the IEEE International Conference on Data Mining (ICDM 2008)},
  booktitle = {ICDM 2008},
  pages = {1007-1012},
  editors = {Giannotti, F. , D. Gunopulos, F. Turini, C. Zaniolo, N. Ramakrishnan, X. Wu},
  publisher = {IEEE Computer Society},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Los Alamitos, CA, USA},
  month = dec,
  year = {2008},
  month_numeric = {12}
}