A novel clustering algorithm inspired by membrane computing.
P systems are a class of distributed parallel computing models; this paper presents a novel clustering algorithm, which is inspired from mechanism of a tissue-like P system with a loop structure of cells, called membrane clustering algorithm. The objects of the cells express the candidate centers of...
| Published in: | Scientific World Journal Vol. 2015; pp. 929471 - 929472 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Published: |
Wiley-Blackwell
1/1/2015
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109721560&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109721560 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 1/1/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109721560 NLM25874264 2012972305 10.1155/2015/929471 NLM25874264 PMC4385684 109721560 ppf: 929471 ppct: 1 formats: tig: atl: A novel clustering algorithm inspired by membrane computing. aug: au: Peng, Hong Luo, Xiaohui Gao, Zhisheng Wang, Jun Pei, Zheng sug: ab: P systems are a class of distributed parallel computing models; this paper presents a novel clustering algorithm, which is inspired from mechanism of a tissue-like P system with a loop structure of cells, called membrane clustering algorithm. The objects of the cells express the candidate centers of clusters and are evolved by the evolution rules. Based on the loop membrane structure, the communication rules realize a local neighborhood topology, which helps the coevolution of the objects and improves the diversity of objects in the system. The tissue-like P system can effectively search for the optimal partitioning with the help of its parallel computing advantage. The proposed clustering algorithm is evaluated on four artificial data sets and six real-life data sets. Experimental results show that the proposed clustering algorithm is superior or competitive to k-means algorithm and several evolutionary clustering algorithms recently reported in the literature. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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