Adaptive noise immune cluster ensemble using affinity propagation

Zhiwen Yu, Guoqiang Han, Le Li, Jiming LIU, Jun Zhang

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Cluster ensemble, as one of the important research directions in the ensemble learning area, is gaining more and more attention, due to its powerful capability to integrate multiple clustering solutions and provide a more accurate, stable and robust result [1]. Cluster ensemble has a lot of useful applications in a large number of areas. Although most of traditional cluster ensemble approaches obtain good results, few of them consider how to achieve good performance for noisy datasets. Some noisy datasets have a number of noisy attributes which may degrade the performance of conventional cluster ensemble approaches. Some noisy datasets which contain noisy samples will affect the final results. Other noisy datasets may be sensitive to distance functions.

Original languageEnglish
Title of host publication2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1454-1455
Number of pages2
ISBN (Electronic)9781509020195
DOIs
Publication statusPublished - May 2016
Event32nd IEEE International Conference on Data Engineering, ICDE 2016 - Helsinki, Finland
Duration: 16 May 201620 May 2016

Publication series

Name2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016

Conference

Conference32nd IEEE International Conference on Data Engineering, ICDE 2016
Country/TerritoryFinland
CityHelsinki
Period16/05/1620/05/16

Scopus Subject Areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Graphics and Computer-Aided Design
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

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