MATLAB实现的带动画效果的KMeans算法.docx
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MATLAB实现的带动画效果的KMeans算法
效果图
实现代码
functionKMeans（）
%KMEANSSummaryofthisfunctiongoeshere
%Detailedexplanationgoeshere
globalin
in=fopen（'KMeans.txt','r'）;
n=1000;
k=6;
d=2;
times=1000;
pts=fscanf（in,'%f',[d,n]）;
kpts=rand（d,k）*1000
figure1=figure（'Color',[111]）;
S=['rgbcky'];
fori=1:
times
i
cnt=zeros（1,k）;
forj=1:
n
t=dist（kpts',pts（:
j））;
p=find（t==min（t））;
f（j）=p;
cnt（p）=cnt（p）+1;
end
kpts=zeros（d,k）;
forj=1:
n
kpts（:
f（j））=kpts（:
f（j））+pts（:
j）;
end
forj=1:
k
ifcnt（j）>0
kpts（:
j）=kpts（:
j）/cnt（j）;
else
kpts（:
j）=rand（d,1）;
end
end
pause（0.5）;
clf
forj=1:
n
holdon
plot（pts（1,j）,pts（2,j）,['*',S（f（j））],'LineWidth',0.3）;
end
forj=1:
k
holdon
plot（kpts（1,j）,kpts（2,j）,['v',S（j）],'LineWidth',0.3）;
end
end
curve_new（input）;
fclose（in）;
end
数据文件（KMeans.txt）
478.0182193816786-807.7627237797763-692.1911562122405
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147.92654823651037-95.30166615175722810.4594863068869
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139.00148313929594121.32302026111165255.89775823224363
239.491285020747-65.324844649012-752.5389734192929
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501.8245886079526-556.0175230235493-520.1316223050567
597.9469198904033-203.30705768435303-330.02137582663374
867.7317125452475-425.5450487080277282.7979237665436
286.06582675537015-807.281893051077-817.8330364485338
-11.181951011967044275.31581452654564257.92600605064104
-862.8896423511706995.901517800711-770.595349743485
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-895.0062856154871-790.172387698572562.4930010799651
234.42845916420697-585.4318780970677-188.92905618581392
799.449811684844264.3626305768603-951.184183350765
556.454612387650161.38536453835468-899.9374098549913
281.14489497712634850.8588849892253137.24742933611356
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282.9534311618279624.5308778433814-707.3461213805765
653.4810903756281426.8791725781582668.9442954186452
202.59776572381224-240.90034240910893456.668471018716
994.4722169098711161.088965690838-782.9111006050575
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353.490744126143370.5248133193538-892.9007788637626
454.1443902596718445.4958277001099430.5361496742696
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109.59230228801448497.90989687274123-430.4137165919508
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116.99524894708088-609.3149069449623417.13234037474353
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331.7393212066322-857.2690520798807-21.457883931763604
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71.52022191333162-670.227022254688-713.9611874466309
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297.0417239391454-863.7850493746723921.7606798446886
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903.3474203741523-625.9355671281719-326.17929789799337
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633.4920023138759-641.0605652284298-76.24115389510314
731.8127112619461640.6482990636155401.95719776766236
838.9502852603646-956.5296323678681-663.6818241336027
816.4011403493546-856.3765595776582101.89264773090372
803.8906978645493-958.9053816135944-284.9170443746409
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869.760113351506384.0998933386777-496.7580819220878
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23.972244015247043-767.5938881117221-618.5712842891853
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264.86520957687344-870.4976658555884-984.8464498214676
26.932399927544793245.2902026088993427.66545692129739
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538.9479069662207-304.2562481389532270.85274793690905
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461.7829752379207506.65530955927807-59.35835348294984
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348.3613664142106-975.036645080799-760.8265884513401
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91.71665752259446-297.8146286030091757.68530706021352
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392.7110557609808-272.3348060837516819.1137668091392
120.29280500860045-817.2709495028034-688.7309173698734
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597.2314440272369-654.3525689841676106.2287344905551
139.1028734327674-359.46994726716025788.9126509996543
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730.8881411385448-380.187117802099-916.5597445978342
627.6565217834805-949.2246177244701-99.24598972743252
529.9795996660584767.3872008921448-979.5561557671685
797.4977279053091853.5093142528758960.4868699367025
724.7626961106043781.1890574334514-702.8250353465551
732.8293406122391-696.0988911137761-337.5462737294313
128.48349958095832935.594318491494-915.9234494180217
910.0661770226748765.0206607707946657.3010197688689
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625.9030964018027166.0532958463366-806.0231948384071
53.46502039906068-657.051725297621959.51926694103395
289.6666380064998544.385104561468324482.8236555199576
611.5334140933708315.41628236401223.7188910747310047
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705.610293449814-288.938764816109-629.5393538143217
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797.1840160945005715.4483066889079138.02949802671583
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196.89106028679498238.49741819505425975.1785386811403
232.3863085366529-889.2328068873929-11.67516273039655
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897.3769
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