Parallel Semi-Supervised Multi-Ant Colonies Clustering Ensemble Based on MapReduce Methodology

Abstract

Semi-supervised clustering ensemble has emerged as an important elaboration of classical clustering problem that improves quality and robustness in clustering by combining the results of different clustering components with user provided constraints. MapReduce is a parallel programming model for processing big data using large numbers of distributed computers (nodes). In this paper, we propose a novel semi-supervised multi-ant colonies consensus clustering algorithm and implement the parallelization of this algorithm using MapReduce on Hadoop platform. Our method incorporates pairwise constraints not only in each ant colony clustering process, but also in computing new similarity matrix during the process of the multi-ant colonies ensemble. In addition, it enhances the computational efficiency for big data by adopting a MapReduce Framework. Experimental results demonstrate the effectiveness of the proposed method.