An Improved Consensus Clustering Algorithm Based on Cell-Like P Systems With Multi-Catalysts

Consensus clustering algorithm, which integrates several clustering results obtained by common algorithms, can find a better result that is independent on parameter settings. However, this kind of algorithm is often designed based on simple, such as
$K$
-means, algorithms, which is limited by the time complexity. In this work, a P system, a novel branch of bio-inspired computing with inherent parallel and distributed computation, is combined with the consensus clustering algorithm. As a result, an improved consensus clustering algorithm is constructed using the hierarchical membrane structure and parallel evolution mechanism in a cell-like P system with multi-catalysts, where the catalysts are utilized to regulate the parallelism of objects evolution. The integration strategy of the algorithm is based on a revised PAM where only the
$q$
-nearest neighbors of the original medoids are considered as candidates for the new medoids. The experimental results indicate that the clustering quality of the proposed algorithm is more robust than well-known consensus clustering algorithms on data sets with noises and outliers. This work gives evidence that the effectiveness and efficiency of consensus clustering algorithms can be improved using P systems.