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 language | English |
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Pages | 788-793 |
Number of pages | 6 |
State | Published - 2002 |
Event | 2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States Duration: 12 May 2002 → 17 May 2002 |
Conference
Conference | 2002 International Joint Conference on Neural Networks (IJCNN '02) |
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Country/Territory | United States |
City | Honolulu, HI |
Period | 12/05/02 → 17/05/02 |
Keywords
- ART
- Cluster analysis
- Clustering
- Hierarchical partitioning
- Unsupervised learning