Limits...
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.


Four artificial data sets: (a) AD_5_2; (b) Data_9_2; (c) Square_4; (d) Sym_3_22.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4385684&req=5

fig4: Four artificial data sets: (a) AD_5_2; (b) Data_9_2; (c) Square_4; (d) Sym_3_22.

Mentions: In the experiments, two kinds of data sets are used to evaluate these clustering algorithms. First is the four manually generated data sets used in the existing literatures, AD_5_2, Data_9_2, Square_4, and Sym_3_22, shown in Figure 4. Second is the six real-life data sets provided in UCI [40], including the Iris, BreastCancer, Newthyroid, LungCancer, Wine, and LiveDisorder. The sizes of the data sets can be found in Table 1.


A novel clustering algorithm inspired by membrane computing.

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

Four artificial data sets: (a) AD_5_2; (b) Data_9_2; (c) Square_4; (d) Sym_3_22.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig4: Four artificial data sets: (a) AD_5_2; (b) Data_9_2; (c) Square_4; (d) Sym_3_22.
Mentions: In the experiments, two kinds of data sets are used to evaluate these clustering algorithms. First is the four manually generated data sets used in the existing literatures, AD_5_2, Data_9_2, Square_4, and Sym_3_22, shown in Figure 4. Second is the six real-life data sets provided in UCI [40], including the Iris, BreastCancer, Newthyroid, LungCancer, Wine, and LiveDisorder. The sizes of the data sets can be found in Table 1.

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.