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1 ق م يات کارБRد ی ا ن سال زد م ماره وم ( ی ٥٠ ) پا ز ٧-١ ٩٥ ص ص شاپا ٧٢٨٦-٢٢٥١ * : :. (HSA)... HSA.. k-means : 1....[1]..[] K-means K-means. K-means.. 1 * shahrar.mr@gmal.com :

2 م وی ر و ی ور ا ل БR ی داده ندی و ی БRای و ل ا ارا یاری GA PSO SA K-means.[4 3]. K-means GA. PSO (HSA). HSA... :. 4 HSA (1) ( ) d X, Y = ( x y ) m = 1 { } K S = x 1, x,..., x n n N { } C = c 1, c,..., ck { 1 } C φ,,,k { 1 } C C φ j and, j,,,k j = (1) : : : :

3 ق م يات کارБRد ی ا ن ٥٠ (١٣٩٥) ١-٧ C = S k = 1. () K () (MSE) ( ) N f X, C = Mn{ X C j = 1,,, K }. = 1 l.[5]..[13] C l X X C l () (HSA) 3. [13] HSA. HSA.. : (HM) HSA. HSA HSA 3

4 م وی ر و ی ور ا ل БR ی داده ندی و ی БRای و ل ا ارا یاری HM (HMCR).. HM [6]. PHMCR = HMCR. [ 0/ 75, 0/ 95] (r pa )... [ 0/ 1, 0/ 5] r pa [10].. (6).. P = HMCR r pa pa. (3) 5-3..[6,9]. Prand = 1 HMCR (4) : Parameter Settng (number of teraton,pop Sze,HMCR, Best soluton = [ ] for I = 1 to number of Pop Sze do HM(I)=Randomly ftness HM (I)=evaluate(HM (I)) 4 p pa, β )

5 ق م يات کارБRد ی ا ن ٥٠ (١٣٩٥) ١-٧ for t = 1 to number of teraton do for I = 1 to number of Pop Sze do for j = 1 to nvar do f rand< HMCR HM=choose a soluton randomly If rand< p pa HM(j)= HM(j)+unform(-1,1). β Else HM(j)= HM(j) end f Else HM(J)= Randomly end f HSA 4 HSA 0.. HSA. () (HMCR) (Maxmp) (HM) MATLAB. (1) (Pac). HM Maxmp HMCR Pac /91 1/5 HSA CMC Vowel Wne Irs [8 7] 5

6 م وی ر و ی ور ا ل БR ی داده ندی و ی БRای و ل ا ارا یاری. K-means SA GA PSO HSA Irs Wne ۱۶۲۹۹ ۰۰۰۷ Vowel ۱۴۹۳۹۳ ۵۸ CMC ۵۶۹۶ ۹۴۶۰ Irs (). () CMC Vowel Wne Irs HSA Wne. Vowel HSA CMC HSA.. K- K-means 6

7 ق م يات کارБRد ی ا ن ٥٠ (١٣٩٥) ١-٧ K- means. means [1] Hatamlou, A., Abdullah, A.,and Nezamabad-pour, S. "A combned approach for clusterng based on K-means and gravtatonal search algorthms" Swarm and Evolutonary Computaton, (6), pp. 47 5, 01. [] Anl, K.J. "Data clusterng: 50 years beyond K-means" Pattern Recognton Letters, (31), pp , 010. [3] Maulk U., and Bandyopadhyay, S. "Genetc algorthm-based clusterng technque" Pattern Recognton, (33), pp , 000. [4] Paterln, S., and Krnk, T. "Dfferental evoluton and partcle swarm optmzaton n parttonal clusterng" Computatonal Statstcs and Data Analyss, (50), pp , 006. [5] Nknam, T., and Amr, B., "An effcent hybrd approach based on PSO, ACO and k-means for cluster analyss" Appled Soft Computng, (10), pp , 010. [6] Geem Z.W., Km J.H., and Loganathan G.V. "A new heurstc optmzaton algorthm, harmony search" Smulaton, (76), pp , 001. [7] Merz, C.J., and Blake, C.L. UCI Repostory of Machne Learnng Databases. Avalable from: [8] N. Sharma, A. Bajpa and R. Ltoruya, Comparson the varous clusterng algorthms of weka tools, Internatonal Jornal of Emergng technology and Advanced Engneerng, vol., no. 5, (01) May. [9] M. E. Celeb, H. A. Kngrav, and P. A. Vela, A comparatve study of effcent ntalzaton methods for the k-means clusterng algorthm, Expert Systems wth Applcatons, vol. 40, no. 1, pp , 013. [10] E. J. Dres and G. L. Peterson, Scalng ant colony optmzaton wth herarchcal renforcement learnng parttonng, n Proceedngs of the 10th Annual Genetc and Evolutonary Computaton Conference (GECCO '08), pp. 5 3, BbloBazaar, Atlanta, Ga, USA, July 008 [11] P. Shen and C. L, Dstrbuted nformaton theoretc clusterng, SIGNAL PROCESSING, IEEE Transactons on, vol. 6, no. 13, pp , 014. [1] G. D Fatta, F. Blasa, S. Cafero, and G. Fortno, Fault tolerant decentralsed k-means clusterng for asynchronous large-scale networks, Journal of Parallel and Dstrbuted Computng, vol. 73, no. 3, pp , 013. [13] H. Mashayekh, J. Habb, S. Voulgars, and M. van Steen, Goscan: Decentralzed scalable data clusterng, Computng, vol. 95, no. 9, pp ,