Abstract: For the uncertain problem that intercluster distance（intercluster separation） influences on clustering in the soft subspace clustering process, a self-adaptive soft subspace clustering algorithm has been proposed based on the compactness of intracluster compactness and the intercluster distance. Minimize the intracluster compactness, and meanwhile maximize the intercluster distance based on the framework of classical k-means clustering algorithm. And a new way of computing clusters’ centers and features weighting is gotten by derivation. This way overcomes the sensitive defect of input parameters, realizes the self-adaptive learning, and obtains better clustering results.

Key words: adaptivity, intercluster distance, soft subspace clustering, high-dimensional data

摘要： For the uncertain problem that intercluster distance（intercluster separation） influences on clustering in the soft subspace clustering process, a self-adaptive soft subspace clustering algorithm has been proposed based on the compactness of intracluster compactness and the intercluster distance. Minimize the intracluster compactness, and meanwhile maximize the intercluster distance based on the framework of classical k-means clustering algorithm. And a new way of computing clusters’ centers and features weighting is gotten by derivation. This way overcomes the sensitive defect of input parameters, realizes the self-adaptive learning, and obtains better clustering results.

关键词: adaptivity, intercluster distance, soft subspace clustering, high-dimensional data