  Fuzzy c-means based clustering algorithms are frequently used for
Takagi-Sugeno-Kang (TSK) fuzzy classifier antecedent parameter estimation. One
rule is initialized from each cluster. However, most of these clustering
algorithms are unsupervised, which waste valuable label information in the
training data. This paper proposes a supervised enhanced soft subspace
clustering (SESSC) algorithm, which considers simultaneously the within-cluster
compactness, between-cluster separation, and label information in clustering.
It can effectively deal with high-dimensional data, be used as a classifier
alone, or be integrated into a TSK fuzzy classifier to further improve its
performance. Experiments on nine UCI datasets from various application domains
demonstrated that SESSC based initialization outperformed other clustering
approaches, especially when the number of rules is small.
