Abstract
Consensus clustering is gaining increasing attention for its high quality and robustness. In particular, K -means-based Consensus Clustering (KCC) converts the usual computationally expensive problem to a classic K -means clustering with generalized utility functions, bringing potentials for large-scale data clustering on different types of data. Despite KCC’s applicability and generalizability, implementing this method such as representing the binary data set in the K -means heuristic is challenging, and has seldom been discussed in prior work. To fill this gap, we present a MATLAB package, KCC, that completely implements the KCC framework, and utilizes a sparse representation technique to achieve a low space complexity. Compared to alternative consensus clustering packages, the KCC package is of high flexibility, efficiency, and effectiveness. Extensive numerical experiments are also included to show its usability on real-world data sets.