A supervisory approach to semi-supervised clustering

Bryan Conroy, Yongxin Taylor Xi, Peter Ramadge

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

2 Scopus citations

Abstract

We propose a new approach to semi-supervised clustering that utilizes boosting to simultaneously learn both a similarity measure and a clustering of the data from given instancelevel must-link and cannot-link constraints. The approach is distinctive in that it uses a supervising feedback loop to gradually update the similarity while at the same time guiding an underlying unsupervised clustering algorithm. Our approach is grounded in the theory of boosting. We provide three examples of the clustering algorithm on real datasets.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
Pages1858-1861
Number of pages4
DOIs
StatePublished - 2010
Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
Duration: Mar 14 2010Mar 19 2010

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
CountryUnited States
CityDallas, TX
Period3/14/103/19/10

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Algorithms
  • Clustering methods
  • Learning systems
  • Pattern classification

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