Consensus Function Based on Matrix Factorization

Abstract

Clustering ensemble has been known as an effective method to improve the robustness and stability of clustering analysis. Clustering ensemble solves the problem in two steps:firstly,generating a large set of clustering partitions based on the clustering algorithms;secondly,combining them using a consensus function to get the final clustering result. The key technology of clustering ensemble is the proper consensus function. Recent research proposed using the matrix factorization to solve clustering ensemble. In this paper, we firstly analyze some traditional matrix factorization algorithms; secondly, we propose a new consensus function using binary nonnegative matrix factorization (BMF) and give the optimization algorithm of BMF; lastly, we propose the new framework of clustering ensemble algorithm and give some experiments on UCI Machine Learning Repository. The experiments show that the new algorithm is effective and clustering performance could be significantly improved.