关键词: 高维数据, 聚类, 投影子空间, 自适应性, 特征权重

Abstract: Due to the curse of dimensionality, many traditional algorithms cannot effectively cluster high dimensional data. In recent years, projective clustering methods spark wide interest. Therein, soft subspace clustering methods have been widely studied and applied. However, most of existing algorithms often require the users to set some important parameters in advance, and ignore the optimization problems of the projected subspace, thus affecting the performance of clustering algorithms. To address the problems, this paper proposes a new objective function, which aims at both minimizing the within-cluster compactness and optimizing the projected subspace associated with each cluster. A new expression for feature-weight computation is mathematically derived, with which a new adaptive projective clustering algorithm is defined based on the framework of classical k-means. In the process of clustering, the optimal values of parameters are automatically calculated, relying on datasets and the formula derived. The experimental results show that the proposed algorithm significantly improves the clustering quality and outperforms the other existing projective clustering algorithms.

Key words: high-dimensional data, clustering, projected subspace, adaptability, feature weighting