关键词: 高维数据, k均值, 软子空间算法, 子空间差异

Abstract: Soft subspace clustering is an important part and research hotspot in clustering research. Clustering in high dimensional space is especially difficult due to the sparse distribution of the data and the curse of dimensionality. By analyzing limitations of the existing algorithms, the concept of subspace difference is proposed. Based on these, a new objective function is given by taking into account the compactness of the subspace clusters and subspace difference of the clusters. And a subspace clustering algorithm based on k-means is presented. The additional parameter is not necessary in the novel algorithm. Theoretical analysis and experimental results demonstrate that the proposed algorithm significantly improves the accuracy.

Key words: high dimensional data, k-means, subspace clustering, subspace difference