Kernel-based hard clustering methods in the feature space with automatic variable weighting
The paper gives kernel-based hard clustering algorithms in the feature space. The algorithms learn a relevance weight for each variable. Partition and cluster interpretation tools are given. Applications on synthetic and real datasets corroborate the proposed algorithms.
feature space kernel clustering adaptive distances clustering analysis