Abstract
Many algorithms for fuzzy clustering depend on initial guesses of cluster prototypes, and on assumptions made as to the number of subgroups present in the data. This study reports on a method for carrying out fuzzy classification without apriori assumptions on the number of clusters in the data set. Assessment of cluster validity is based on performance measures using hypervolnme and density criteria. The new algorithm is derived from a combination of the fuzzy K-means algorithm and the fuzzy maximum likelihood estimation (FMLE). The UFP-ONC (unsupervised fuzzy partitionoptimal number of classes) algorithm performs well in situations of large variability of cluster shapes, densities, and number of data points in each cluster. It has been tested on a number of simulated and real data sets.
| Original language | English |
|---|---|
| Pages (from-to) | 773-780 |
| Number of pages | 8 |
| Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| Volume | 11 |
| Issue number | 7 |
| DOIs | |
| State | Published - 1 Jan 1989 |
| Externally published | Yes |
Keywords
- Clustering of sleep EEG
- fuzzy clustering
- hyperelliptoidal clusters
- performance measures for cluster validity
- unequally variable features
- unsupervised tracking of cluster prototype
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Computational Theory and Mathematics
- Artificial Intelligence
- Applied Mathematics