Locally discriminative coclustering

Lijun Zhang, Chun Chen, Jiajun Bu, Zhengguang Chen, Deng Cai, Jiawei Han

Research output: Contribution to journalArticlepeer-review


Different from traditional one-sided clustering techniques, coclustering makes use of the duality between samples and features to partition them simultaneously. Most of the existing co-clustering algorithms focus on modeling the relationship between samples and features, whereas the intersample and interfeature relationships are ignored. In this paper, we propose a novel coclustering algorithm named Locally Discriminative Coclustering (LDCC) to explore the relationship between samples and features as well as the intersample and interfeature relationships. Specifically, the sample-feature relationship is modeled by a bipartite graph between samples and features. And we apply local linear regression to discovering the intrinsic discriminative structures of both sample space and feature space. For each local patch in the sample and feature spaces, a local linear function is estimated to predict the labels of the points in this patch. The intersample and interfeature relationships are thus captured by minimizing the fitting errors of all the local linear functions. In this way, LDCC groups strongly associated samples and features together, while respecting the local structures of both sample and feature spaces. Our experimental results on several benchmark data sets have demonstrated the effectiveness of the proposed method.

Original languageEnglish (US)
Article number5740883
Pages (from-to)1025-1035
Number of pages11
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number6
StatePublished - 2012


  • Coclustering
  • bipartite graph
  • clustering
  • local linear regression

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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