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A novel clustering algorithm inspired by membrane computing.

Peng H, Luo X, Gao Z, Wang J, Pei Z - ScientificWorldJournal (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Center for Radio Administration and Technology Development, Xihua University, Chengdu 610039, China.

ABSTRACT
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.

No MeSH data available.


Evolution procedure of objects in a cell.
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fig2: Evolution procedure of objects in a cell.

Mentions: The role of evolution rules is to evolve the objects in cells to generate new objects used in next computing step. During the evolution, each cell maintains the same size (the number of objects). In this work, three known genetic operations (selection, crossover, and mutation) [38, 39] are used as the evolution rules in cells. In a computing step, all objects (located in object pool) in each cell and the best objects (located in external pool) from its two adjacent cells constitute a matching pool. The objects in external pool are actually the best objects communicated from its two adjacent cells in previous computing step. The objects in matching pool will be evolved by executing selection, crossover, and mutation operations in turn. In order to maintain the size of objects in each cell, truncation operation is used to constitute new object pool according to the M values of objects. The objects in new object pool will be regarded as the objects to be evolved in next computing step. Figure 2 shows the evolution procedure of objects in a cell.


A novel clustering algorithm inspired by membrane computing.

Peng H, Luo X, Gao Z, Wang J, Pei Z - ScientificWorldJournal (2015)

Evolution procedure of objects in a cell.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4385684&req=5

fig2: Evolution procedure of objects in a cell.
Mentions: The role of evolution rules is to evolve the objects in cells to generate new objects used in next computing step. During the evolution, each cell maintains the same size (the number of objects). In this work, three known genetic operations (selection, crossover, and mutation) [38, 39] are used as the evolution rules in cells. In a computing step, all objects (located in object pool) in each cell and the best objects (located in external pool) from its two adjacent cells constitute a matching pool. The objects in external pool are actually the best objects communicated from its two adjacent cells in previous computing step. The objects in matching pool will be evolved by executing selection, crossover, and mutation operations in turn. In order to maintain the size of objects in each cell, truncation operation is used to constitute new object pool according to the M values of objects. The objects in new object pool will be regarded as the objects to be evolved in next computing step. Figure 2 shows the evolution procedure of objects in a cell.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Center for Radio Administration and Technology Development, Xihua University, Chengdu 610039, China.

ABSTRACT
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.

No MeSH data available.