A hierarchical approach to ART-like clustering algorithm

Mu Chun Su, Yi Chun Liu

Research output: Contribution to conferencePaperpeer-review

6 Scopus citations

Abstract

In this paper, we propose a hierarchical approach to ART-like clustering algorithm which is able to deal with data consisting of arbitrarily geometrical-shaped clusters. Combining hierarchical and ART-like clustering is suggested as a natural feasible solution to the two problems of determining the number of clusters and clustering data. A 2-d artificial data set is tested to demonstrate the performance of the proposed algorithm.

Original languageEnglish
Pages788-793
Number of pages6
StatePublished - 2002
Event2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States
Duration: 12 May 200217 May 2002

Conference

Conference2002 International Joint Conference on Neural Networks (IJCNN '02)
Country/TerritoryUnited States
CityHonolulu, HI
Period12/05/0217/05/02

Keywords

  • ART
  • Cluster analysis
  • Clustering
  • Hierarchical partitioning
  • Unsupervised learning

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