Multiobjective fuzzy clustering approach based on tissue-like membrane systems

An extended clustering membrane system using a cell-like P system with active membranes based on particle swarm optimization (PSO), named PSO-CP, is designed, developed, implemented, and tested. e purpose of PSO-CP is to solve clustering problems. In PSO-CP, evolution rules based on the standard PSO mechanism are used to evolve the objects and communication rules are adopted to accelerate convergence and avoid prematurity. Subsystems of membranes are generated and dissolved by the membrane creation and dissolution rules, and a modified PSO mechanism is developed to help the objects escape from local optima. Under the control of the evolution-communication mechanism, the extended membrane system can effectively search for the optimal partitioning and improve the clustering performance with the help of the distributed parallel computing model. is extended clustering membrane system is compared with five existing PSO clustering approaches using ten benchmark clustering problems, and the computational results demonstrate the effectiveness of PSO-CP.

Section: Comparison With Other Clustering Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Extended Clustering Membrane System Based on Particle Swarm Optimization and Cell-Like P System with Active Membranes

Wang

Liu

Sun

et al. 2020

“…Many scholars focused on the study of multiobjective clustering to overcome the defect of conventional clustering algorithms. Peng et al [18] proposed fuzzy multiobjective clustering based on PSO to obtain wellseparated, connected, and compact clusters. Saha and Maulik [19] proposed the multiobjective clustering based on incremental learning for categorical data.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hierarchical Clustering Approach Based on Universal Gravitation

Zhang

She

2020

The target of the clustering analysis is to group a set of data points into several clusters based on the similarity or distance. The similarity or distance is usually a scalar used in numerous traditional clustering algorithms. Nevertheless, a vector, such as data gravitational force, contains more information than a scalar and can be applied in clustering analysis to promote clustering performance. Therefore, this paper proposes a three-stage hierarchical clustering approach called GHC, which takes advantage of the vector characteristic of data gravitational force inspired by the law of universal gravitation. In the first stage, a sparse gravitational graph is constructed based on the top k data gravitations between each data point and its neighbors in the local region. Then the sparse graph is partitioned into many subgraphs by the gravitational influence coefficient. In the last stage, the satisfactory clustering result is obtained by merging these subgraphs iteratively by using a new linkage criterion. To demonstrate the performance of GHC algorithm, the experiments on synthetic and real-world data sets are conducted, and the results show that the GHC algorithm achieves better performance than the other existing clustering algorithms.

“…In recent years, applications of membrane computing have attracted a lot of attention from researchers [19][20][21][22]. There are also some other applications; for example, membrane systems are used to solve multiobjective fuzzy clustering problems [23], solve unsupervised learning algorithms [24], solve automatic fuzzy clustering problems [25], and solve the problems of fault diagnosis of power systems [26]. Liu et al [27] proposed an improved Apriori algorithm based on an Evolution-Communication tissue-Like P System.…”

Section: Introductionmentioning

confidence: 99%

A Density Peak Clustering Algorithm Based on the K‐Nearest Shannon Entropy and Tissue‐Like P System

Jiang

Liu

Sun

2019

This study proposes a novel method to calculate the density of the data points based on K-nearest neighbors and Shannon entropy. A variant of tissue-like P systems with active membranes is introduced to realize the clustering process. The new variant of tissue-like P systems can improve the efficiency of the algorithm and reduce the computation complexity. Finally, experimental results on synthetic and real-world datasets show that the new method is more effective than the other state-of-the-art clustering methods.