A novel spectral clustering algorithm

Kan Li*, Yushu Liu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In the spectral clustering algorithm, determination of cluster number is a difficult problem. Here an autonomous spectral clustering algorithm is proposed. Eigengap is used to discover the clustering stability and decide automatically the cluster number, which is proved theoretically the rationality of cluster number. A kernel based fuzzy c-means is introduced to spectral clustering algorithm. Finally our algorithm compares with c-means, Ng et.al's algorithm and Francesco et.al's algorithm in the UCI data sets(IRIS data and Wisconsin database). The experiments show our algorithm may get better results.

Original languageEnglish
Title of host publicationProceedings of the 2007 International Conference on Artificial Intelligence, ICAI 2007
Pages365-368
Number of pages4
Publication statusPublished - 2007
Event2007 International Conference on Artificial Intelligence, ICAI 2007 - Las Vegas, NV, United States
Duration: 25 Jun 200728 Jun 2007

Publication series

NameProceedings of the 2007 International Conference on Artificial Intelligence, ICAI 2007
Volume1

Conference

Conference2007 International Conference on Artificial Intelligence, ICAI 2007
Country/TerritoryUnited States
CityLas Vegas, NV
Period25/06/0728/06/07

Keywords

  • Cluster number
  • Eigengap
  • Kernel
  • Spectral clustering

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