Discriminatively embedded fuzzy K-Means clustering with feature selection strategy

Fuzzy K-Means clustering (FKM) is one of the most popular methods to partition data into clusters. Traditional FKM and its extensions perform fuzzy clustering based on original high-dimensional features. However, the presence of noisy and redundant features would cause the degradation of clustering performance. To avoid this problem, we integrate fuzzy clustering and feature selection into a unified model where the structured sparsity-inducing norm is imposed on the transformation matrix to determine the valuable feature subse adaptively. The clustering task and feature selection process are promoted mutually. To solve this model, an iterative algorithm is developed. Extensive experiments conducted on benchmark data sets demonstrate the effectiveness of our proposed method.