Issue No. 07 - July (1989 vol. 11)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.192473
<p>This study reports on a method for carrying out fuzzy classification without a priori assumptions on the number of clusters in the data set. Assessment of cluster validity is based on performance measures using hypervolume and density criteria. An algorithm is derived from a combination of the fuzzy K-means algorithm and fuzzy maximum-likelihood estimation. The unsupervised fuzzy partition-optimal number of classes algorithm performs well in situations of large variability of cluster shapes, densities, and number of data points in each cluster. The algorithm was tested on different classes of simulated data, and on a real data set derived from sleep EEG signal.</p>
pattern recognition; fuzzy set theory; unsupervised optimal fuzzy clustering; fuzzy classification; cluster validity; fuzzy K-means algorithm; fuzzy maximum-likelihood estimation; unsupervised fuzzy partition-optimal number of classes algorithm; sleep EEG signal; electroencephalography; fuzzy set theory; pattern recognition
A. Geva and I. Gath, "Unsupervised Optimal Fuzzy Clustering," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 11, no. , pp. 773-780, 1989.