Combining multiple clusterings via k-modes algorithm

Clustering ensembles have emerged as a powerful method for improving both the robustness and the stability of unsupervised classification solutions. However, finding a consensus clustering from multiple partitions is a difficult problem that can be approached from graph-based, combinatorial or statistical perspectives. A consensus scheme via the k-modes algorithm is proposed in this paper. A combined partition is found as a solution to the corresponding categorical data clustering problem using the k-modes algorithm. This study compares the performance of the k-modes consensus algorithm with other fusion approaches for clustering ensembles. Experimental results demonstrate the effectiveness of the proposed method.