Biclustering multivariate data for correlated subspace mining

Kazuho Watanabe, Hsiang Yun Wu, Yusuke Niibe, Shigeo Takahashi, Issei Fujishiro

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

7 Citations (Scopus)

Abstract

Exploring feature subspaces is one of promising approaches to analyzing and understanding the important patterns in multivariate data. If relying too much on effective enhancements in manual interventions, the associated results depend heavily on the knowledge and skills of users performing the data analysis. This paper presents a novel approach to extracting feature subspaces from multivariate data by incorporating biclustering techniques. The approach has been maximally automated in the sense that highly-correlated dimensions are automatically grouped to form subspaces, which effectively supports further exploration of them. A key idea behind our approach lies in a new mathematical formulation of asymmetric biclustering, by combining spherical k-means clustering for grouping highly-correlated dimensions, together with ordinary k-means clustering for identifying subsets of data samples. Lower-dimensional representations of data in feature subspaces are successfully visualized by parallel coordinate plot, where we project the data samples of correlated dimensions to one composite axis through dimensionality reduction schemes. Several experimental results of our data analysis together with discussions will be provided to assess the capability of our approach.

Original languageEnglish
Title of host publicationIEEE Pacific Visualization Symposium
PublisherIEEE Computer Society
Pages287-294
Number of pages8
Volume2015-July
ISBN (Print)9781467368797
DOIs
Publication statusPublished - 2015 Jul 14
Event2015 8th IEEE Pacific Visualization Symposium, PacificVis 2015 - Hangzhou, China
Duration: 2015 Apr 142015 Apr 17

Other

Other2015 8th IEEE Pacific Visualization Symposium, PacificVis 2015
CountryChina
CityHangzhou
Period15/4/1415/4/17

Keywords

  • biclustering
  • correlation
  • Multivariate data
  • subspaces

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software

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  • Cite this

    Watanabe, K., Wu, H. Y., Niibe, Y., Takahashi, S., & Fujishiro, I. (2015). Biclustering multivariate data for correlated subspace mining. In IEEE Pacific Visualization Symposium (Vol. 2015-July, pp. 287-294). [7156389] IEEE Computer Society. https://doi.org/10.1109/PACIFICVIS.2015.7156389