Constraint projections for semi-supervised spectral clustering ensemble

Jingya Yang, Linfu Sun, Qishi Wu

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Cluster ensemble combines multiple base clustering results in a suitable way to improve the accuracy of the clustering result. In the conventional cluster ensemble frameworks, pairwise constraints and constraint projections have not been used together, and spectral clustering algorithm is rarely adopted to serve as the consensus function. In this paper, we design a constraint projections for semi-supervised spectral clustering ensemble (CPSSSCE) model. It takes advantages of spectral clustering algorithm and executes semi-supervised learning twice. Compared to traditional cluster ensemble approaches, CPSSSCE is characterized by several properties. First, the original data are transformed to lower-dimensional representations by constraint projection before base clustering. Second, a similarity matrix is constructed using the base clustering results and modified using pairwise constraints. Third, the spectral clustering algorithm is applied to process the similarity matrix to obtain a consensus cluster result. Extensive experiments on standard University of California Irvine Machine Learning Repository (UCI) and Microsoft datasets demonstrated that the CPSSSCE is superior to other cluster ensemble algorithms including a semi-supervised spectral clustering ensemble.

Original languageEnglish (US)
Article numbere5359
JournalConcurrency Computation Practice and Experience
Volume31
Issue number20
DOIs
StatePublished - Oct 25 2019

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Computational Theory and Mathematics

Keywords

  • constraint projection
  • pairwise constraints
  • semi-supervised learning (SSL)
  • similarity matrix
  • spectral clustering

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