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

Abstract

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.

A Novel Consensus Fuzzy K-Modes Clustering Using Coupling DNA-Chain-Hypergraph P System for Categorical Data

“…In this section, we firstly select the number of BPs. According to the analysis in previous research [37], we can select the number of BPs as about 50, and the clustering effect gradually stabilizes. In this paper, we also need to determine the number of BPs, but we do not use the same BP generation strategy as in the other methods.…”

“…represents the dataset, and arbitrary cluster algorithms are used p times to get p different basic partitions 1 2 , ,..., p π π π (BPs) [37]. The number of clusters 1 2 , ,..., p K K K in different partition is arbitrary.…”

An Extended Tissue-like P System Based on Membrane Systems and Quantum-Behaved Particle Swarm Optimization for Image Segmentation

“…Furthermore, hierarchical structure based MIEAs, including a nested membrane structure (NMS) [47], one level membrane structure (OLMS) [48], hybrid membrane structure (HMS) [49] and dynamic membrane structure (DMS) [50], are the first kind of EMC. NMS-based MIEAs are designed to integrate cell-like P systems with different metaheuristic techniques, such as GA [51], DE [52,53], ACO [54], and so on [55,56]. OLMSbased MIEAs are proposed to combine cell-like P systems and different algorithms in a membrane (AIM), such as GA [57], DE [58], PSO [59,60], ACO [61], and QIEAS [62].…”