Soft Subspace Clustering with a Multi-objective Evolutionary Approach

Abstract

In recent years, the problem, which copes with high-dimensional data by the method of cluster analysis, has become a focus and difficulty in the field of artificial intelligence. Many conventional soft subspace clustering techniques merge several criteria into a single objective to improve performance, however, the weighting parameters become important but difficult to set. A novel soft subspace clustering with a multi-objective evolutionary approach (MOSSC) is proposed to this problem. First, two new objective function is constructed by minimizing the within-cluster compactness and maximizing the between-cluster separation based on the framework of soft subspace clustering algorithm. Based on this objective function, a new way of computing clusters' feature weights, centers and membership is then derived by using Lagrange multiplier method. The properties of this algorithm are investigated and the performance is evaluated experimentally using UCI datasets.