Abstract
Several cluster validity measures have been proposed for evaluating clustering results. However, existing methods may not work well for the following two kinds of data sets. The first one is that the data set contains cluster groups with different densities. The second one is that some of the cluster groups are closely positioned. In this paper, we introduce a new cluster validity index. In this method, we define the index as the ratio between the squared total length of the data eigen-axes and the between-cluster separation. Compared with exiting cluster validity indices, the proposed index produces more accurate results and is able to handle the two kinds of data sets mentioned above. © 2005 IEEE.
| Original language | English |
|---|---|
| Pages (from-to) | 798-803 |
| Journal | Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics |
| Volume | 1 |
| DOIs | |
| Publication status | Published - 2005 |
| Event | IEEE Systems, Man and Cybernetics Society, Proceedings - 2005 International Conference on Systems, Man and Cybernetics - Waikoloa, HI, United States Duration: 10 Oct 2005 → 12 Oct 2005 |
Research Keywords
- Cluster Validity
- Clustering
- Data Classification
- Unsupervised Learning
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