|Table of Contents|

Cooperative Evolutionary K-means Clustering Algorithm With Multi-populations(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2010年03期
Page:
122-126
Research Field:
计算机科学
Publishing date:

Info

Title:
Cooperative Evolutionary K-means Clustering Algorithm With Multi-populations
Author(s):
Qu Jianhua1; Shao Zengzhen2
1.School of Management and Economics,Shandong Normal University,Jinan 250014,China 2. School of Inform at ion Science and Engin eering, Shandong N orm alUn ivers ity, Jinan 250014, C h ina
Keywords:
mu lt-i population;  PSO;  K-m eans;  cooperative evo lu tion
PACS:
TP301.6
DOI:
-
Abstract:
Th is paper presents an m ixed c lustering a lgor ithm based on cooperative evo lution w ith mu lt-i populations. It adopts cooperativ e evo lutionary strategyw ith mu lt-i popu lations to chang e them ode o f trad itional search ing optim um so lutions. The whole cluster ing process is d iv ided into tw o stages. The first stag e uses the cooperative evo lutiona ry PSO algorithm to search the in itia l c lustering centers. The second stage uses theK-m eans algor ithm. The exper im ent prov ed that this m ethod w as ab le to ex trac t the correct number o f c lusters w ith good c lustering qua lity com pa red to the results obta ined from other cluster ing a lgor ithm s likeK-m eans and PSO c luste ring a lgor ithm.

References:

[ 1] LiM J, NgM K, Cheung Y M, e t a.l Agg lom era tive fuzzyK-m eans cluster ing algor ithm w ith se lection o f number of c lusters [ J] . IEEE T ransactions on Know ledge and Da ta Eng ineer ing, 2008, 20( 11): 1 519-1 534.
[ 2] LaszloM, M ukher jee S. A gene tic algor ithm that ex changes neighbo ring centers forK-m eans cluster ing [ J]. Pattern Recogn-i tion Lette rs, 2007, 28( 16): 2 359-2 366.
[ 3] ArthurD, Vassilv itskiiS. K-m eans+ + : the advantages of careful seed ing[ C ] / / Proceedings of the 18 th annualACM-S IAM Sym pos ium on D iscrete a lgo rithm s. New Or leans: Soc iety for Industr ia l and App liedM athem atics, 2007: 1 027-1 035.
[ 4] L ikas A, V lassisN, V erbeek J. The g lobalK-m eans c lustering a lgor ithm [ J] . Pattern Recogn ition, 2003, 36( 2): 451-461.
[ 5] Kennedy J, Eberhart R, Sh iY. Sw arm Intelligence[M ] . [ S. .l ]: M o rgan Kaufm ann Publishers, 2001.
[ 6] de Castro L N, Von Zuben F J. Recent Deve lopm ents in B io log ically Insp ired Com puting[M ]. London: Idea Group Inc, 2004,
[ 7] Bonabeau E, DorigoM, Th?rau laz G. Sw arm Inte llig enceFrom Natura l toArtific ia l System s[M ] . [ S. .l ]: Ox fo rd Un iversity Press, 1999.
[ 8] Kennedy J, Eberhart R C. Partic le swa rm optim ization[ C] / / Proceed ings of IEEE Internationa l Conference on N eural Ne-t w orks, IV. Perth: IEEE, 1995: 1 942-1 948.
[ 9] Van derM erwe DW, Enge lbrech t A P. Data cluster ing using partic le sw arm optim ization[ C ] / / Proceed ings of IEEE Cong ress on Evolutionary Com putation 2003 ( CEC 2003), Canberra: IEEE, 2003: 215-220.
[ 10] Shubham Agraw a,l Pan ig rah i B K, M ano jKum ar T iw ar.i M ultiob jective particle sw arm a lgor ithm w ith fuzzy c lustering for elec trical pow er dispa tch[ J]. IEEE Transac tions on E vo lutiona ry Computation, 2008, 12( 5): 529-541.
[ 11] Chang Dongx ia, Zhang Xianda, Zheng Changw en. A genetic a lgo rithm w ith g ene rearrangem ent forK-means cluster ing [ J]. Pattern Recogn ition, 2009, 42( 7): 1 210-1 222.
[ 12] La i J Z C, H uang T J, Liaw Y C. A fastK-m eans cluster ing a lgo rithm using c luster center d isp lacement [ J]. Pa ttern Recogn ition, 2009, 42( 11): 2 551-2 556.

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Last Update: 2013-04-08