An Improved PSO-Based Clustering Algorithm Inspired by Tissue-Like P System

Gao, Tong; Liu, Xiyu; Wang, Lin

doi:10.1007/978-3-319-93803-5_31

Cited by 5 publications

(7 citation statements)

References 14 publications

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“…Let the number of iterations equals to 4, a data set containing 15 data points is clustered to 3 clusters in this instance. The data set to be processed is as follow: T ins = {(1, 2), (2,8), (2,7), (2,4), (2,2), (3,7), (3,2), (4,3), (5,6), (6,8), (6,7), (6,5), (7,9), (7,8), (7, 7)}. And the data points in T ins in turn correspond to data point marks form ε 1 to ε 15 .…”

Section: Calculate Instancementioning

confidence: 99%

“…As suggested by Fig.9, the temporary clustering results are {ε 1 , ε 4 , ε 5 , ε 7 , ε 8 }, {ε 9 , ε 10 , ε 11 , ε 12 , ε 13 , ε 14 , ε 15 } and {ε 2 , ε 3 , ε 6 } after the fourth iteration. Finally, data set T ins is clustered into {(1, 2), (2, 4), (2, 2), (3, 2), (4, 3)}, {(5, 6), (6,8), (6,7), (6,5), (7,9), (7,8), (7, 7)} and {(2, 8), (2, 7), (3, 7)} as suggested by the scatter plot in Fig.10.…”

Section: Calculate Instancementioning

confidence: 99%

“…P systems are inspired from the compartmental structure and the way to process chemical compounds of alive cells, cells in tissue, organs, etc. Up to now, many variants of P systems have been investigated, mainly including cell-like P systems [1,3,4], tissue-like P systems [5][6][7] and neural-like P systems [8,9]. P systems have been studied in many areas, such as biology [10], linguistics [11], computer science, mathematics [12], etc.…”

Section: Introductionmentioning

confidence: 99%

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A P System for K-Medoids-Based Clustering

Guo¹,

Xie²

2018

ijacsa

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The membrane computing model, also known as the P system, is a parallel and distributed computing system. K-medoids algorithm is one of the most famous algorithms in partition-based clustering algorithms, and has been widely used in data analysis and modern scientific research. Combining the P system with the k-medoids algorithm, the maximum parallelism calculated by the P system can effectively reduce the time complexity of the k-medoids clustering algorithm. Based on this, this paper proposes a cell-like P system with promoters and inhibitors based on k-medoids clustering, and then an instance is given to illustrate the practicability and effectiveness of the P system designed.

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Section: Calculate Instancementioning

confidence: 99%

Section: Calculate Instancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A P System for K-Medoids-Based Clustering

Guo¹,

Xie²

2018

ijacsa

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“…Membrane computing has been applied in various fields, such as language generation, electricity fault diagnosis, and image processing [12]- [15]. Clustering based on membrane systems has shown good convergence, robustness, and parallelism [16]- [19]. Peng et al [16] proposed a tissue-like P system based multiobjective fuzzy clustering algorithm to optimize three objectives simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…Peng et al [18] proposed a novel automatic fuzzy clustering method based on an extended membrane system with active membranes. Gao et al [19] presented an improved PSO-based clustering algorithm inspired by tissue-like P system, called TPCA. Besides, new P systems are also designed to solve more problems.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Chain-Hypergraph P Systems for Multiobjective Ensemble Clustering

et al. 2019

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Clustering is a classic combined optimization problem that is widely used in pattern recognition, image processing, market analysis and so on. However, the efficiency of clustering algorithms decreases as the amount of data increases. In addition, most of the existing methods optimize only one objective and therefore may be suitable only for datasets with certain features. To address these limitations, in this paper, we develop a new hybrid chain-hypergraph P system (named HCHPS), which makes full use of the parallelism of P systems as well as the advantages of chain and hypergraph topology structures for accurate and efficient clustering. Our new P system comprises three types of subsystems, i.e., reaction chain membrane subsystems, local communication membrane subsystems and global ensemble membrane subsystems. Each type of subsystems is implemented end-to-end in HCHPS with new rules and membrane structures in parallel. In particular, to obtain efficient clustering center objects and make the algorithm robust to data with various features, the reaction chain membrane subsystems perform three different multiobjective strategies simultaneously by new chain evolution rules. To increase the population diversity of cluster centers, the local communication membrane subsystems utilize transport rules between membranes for coevolution of nondominated objects. The global ensemble membrane subsystems conduct a new dense representation multisize ensemble strategy to further improve the accuracy of the final results. Evaluations on two artificial data sets and 17 real-life data sets demonstrate the robustness of the proposed method in correctly clustering data sets with different dimensions and shapes. Our experimental results outperform those of both baseline and state-of-the-art methods. Moreover, benefiting from the parallelism, HCHPS is less time consuming than other methods, featuring an average completion time of 28.07 seconds on the 17 real-life data sets. Moreover, an ablation study shows that our proposed components are critical for effective cluster analysis. INDEX TERMS Chain-hypergraph P system, multiobjective optimization, cluster analysis.

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