1. A new approach to clustering data with arbitrary shapes
- Author
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Su, Mu-Chun and Liu, Yi-Chun
- Subjects
- *
CLUSTER analysis (Statistics) , *STATISTICAL correlation , *RANDOM variables , *ALGORITHMS - Abstract
Abstract: In this paper we propose a clustering algorithm to cluster data with arbitrary shapes without knowing the number of clusters in advance. The proposed algorithm is a two-stage algorithm. In the first stage, a neural network incorporated with an ART-like training algorithm is used to cluster data into a set of multi-dimensional hyperellipsoids. At the second stage, a dendrogram is built to complement the neural network. We then use dendrograms and so-called tables of relative frequency counts to help analysts to pick some trustable clustering results from a lot of different clustering results. Several data sets were tested to demonstrate the performance of the proposed algorithm. [Copyright &y& Elsevier]
- Published
- 2005
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