Kmeans算法java代码

package com.nju.yzf;import java.io.BufferedReader;import java.io.FileInputStream;import java.io.IOException;import java.io.InputStreamReader;import java.util.ArrayList;import java.util.List;import java.util.Random;public class Kmeans {/** * @param args * @throws IOException */public static List<ArrayList<ArrayList<Double>>> initHelpCenterList(List<ArrayList<ArrayList<Double>>> helpCenterList,int k){for(int i=0;i<k;i++){helpCenterList.add(new ArrayList<ArrayList<Double>>());}return helpCenterList;}/** * @param args * @throws IOException */public static void main(String[] args) throws IOException{List<ArrayList<Double>> centers = new ArrayList<ArrayList<Double>>();List<ArrayList<Double>> newCenters = new ArrayList<ArrayList<Double>>();List<ArrayList<ArrayList<Double>>> helpCenterList = new ArrayList<ArrayList<ArrayList<Double>>>();//读入原始数据BufferedReader br=new BufferedReader(new InputStreamReader(new FileInputStream("wine.txt")));String data = null;List<ArrayList<Double>> dataList = new ArrayList<ArrayList<Double>>();while((data=br.readLine())!=null){//System.out.println(data);String []fields = data.split(",");List<Double> tmpList = new ArrayList<Double>();for(int i=0; i<fields.length;i++)tmpList.add(Double.parseDouble(fields[i]));dataList.add((ArrayList<Double>) tmpList);}br.close();//随机确定K个初始聚类中心Random rd = new Random();int k=3;int [] initIndex={59,71,48};int [] helpIndex = {0,59,130};int [] givenIndex = {0,1,2};System.out.println("random centers' index");for(int i=0;i<k;i++){int index = rd.nextInt(initIndex[i]) + helpIndex[i];//int index = givenIndex[i];System.out.println("index "+index);centers.add(dataList.get(index));helpCenterList.add(new ArrayList<ArrayList<Double>>());}/*//注释掉的这部分目的是，取测试数据集最后稳定的三个类簇的聚类中心作为初始聚类中心centers = new ArrayList<ArrayList<Double>>();for(int i=0;i<59;i++)helpCenterList.get(0).add(dataList.get(i));for(int i=59;i<130;i++)helpCenterList.get(1).add(dataList.get(i));for(int i=130;i<178;i++)helpCenterList.get(2).add(dataList.get(i));for(int i=0;i<k;i++){ArrayList<Double> tmp = new ArrayList<Double>();for(int j=0;j<dataList.get(0).size();j++){double sum=0;for(int t=0;t<helpCenterList.get(i).size();t++)sum+=helpCenterList.get(i).get(t).get(j);tmp.add(sum/helpCenterList.get(i).size());}centers.add(tmp);}*///输出k个初始中心System.out.println("original centers:");for(int i=0;i<k;i++)System.out.println(centers.get(i));while(true){//进行若干次迭代，直到聚类中心稳定for(int i=0;i<dataList.size();i++){//标注每一条记录所属于的中心double minDistance=99999999;int centerIndex=-1;for(int j=0;j<k;j++){//离0~k之间哪个中心最近double currentDistance=0;for(int t=1;t<centers.get(0).size();t++){//计算两点之间的欧式距离currentDistance+=((centers.get(j).get(t)-dataList.get(i).get(t))/(centers.get(j).get(t)+dataList.get(i).get(t))) * ((centers.get(j).get(t)-dataList.get(i).get(t))/(centers.get(j).get(t)+dataList.get(i).get(t))); }if(minDistance>currentDistance){minDistance=currentDistance;centerIndex=j;}}helpCenterList.get(centerIndex).add(dataList.get(i));}//System.out.println(helpCenterList);//计算新的k个聚类中心for(int i=0;i<k;i++){ArrayList<Double> tmp = new ArrayList<Double>();for(int j=0;j<centers.get(0).size();j++){double sum=0;for(int t=0;t<helpCenterList.get(i).size();t++)sum+=helpCenterList.get(i).get(t).get(j);tmp.add(sum/helpCenterList.get(i).size());}newCenters.add(tmp);}System.out.println("\nnew clusters' centers:\n");for(int i=0;i<k;i++)System.out.println(newCenters.get(i));//计算新旧中心之间的距离，当距离小于阈值时，聚类算法结束double distance=0;for(int i=0;i<k;i++){for(int j=1;j<centers.get(0).size();j++){//计算两点之间的欧式距离distance +=((centers.get(i).get(j)-newCenters.get(i).get(j))/(centers.get(i).get(j)+newCenters.get(i).get(j))) * ((centers.get(i).get(j)-newCenters.get(i).get(j))/(centers.get(i).get(j)+newCenters.get(i).get(j))); }//System.out.println(i+" "+distance);}System.out.println("\ndistance: "+distance+"\n\n");if(distance==0)//小于阈值时，结束循环break;else//否则，新的中心来代替旧的中心，进行下一轮迭代{centers = new ArrayList<ArrayList<Double>>(newCenters);//System.out.println(newCenters);newCenters = new ArrayList<ArrayList<Double>>();helpCenterList = new ArrayList<ArrayList<ArrayList<Double>>>();helpCenterList=initHelpCenterList(helpCenterList,k);}}//输出最后聚类结果for(int i=0;i<k;i++){System.out.println("\n\nCluster: "+(i+1)+"   size: "+helpCenterList.get(i).size()+" :\n\n");for(int j=0;j<helpCenterList.get(i).size();j++){System.out.println(helpCenterList.get(i).get(j));}}}}

测试数据集来源：wine数据集from UCI

以下是某次的运行结果，可以看出聚类结果与参考结果比较吻合

random centers' indexindex 4index 102index 166original centers:[1.0, 13.744745762711865, 2.0106779661016954, 2.455593220338984, 17.037288135593222, 106.33898305084746, 2.8401694915254234, 2.982372881355932, 0.29, 1.8993220338983055, 5.528305084745763, 1.0620338983050848, 3.1577966101694916, 1115.7118644067796][2.0, 12.278732394366198, 1.932676056338028, 2.244788732394365, 20.238028169014086, 94.54929577464789, 2.2588732394366198, 2.080845070422536, 0.363661971830986, 1.6302816901408452, 3.08661971830986, 1.0562816901408452, 2.785352112676055, 519.5070422535211][3.0, 13.153749999999997, 3.3337500000000007, 2.4370833333333333, 21.416666666666668, 99.3125, 1.6787500000000002, 0.7814583333333331, 0.44749999999999995, 1.1535416666666667, 7.396249979166668, 0.6827083333333334, 1.6835416666666658, 629.8958333333334]new clusters' centers:[1.040983606557377, 13.695245901639344, 1.9844262295081967, 2.446475409836067, 17.199999999999996, 106.13934426229508, 2.8595901639344263, 3.0022950819672127, 0.28737704918032797, 1.900901639344262, 5.5009836065573765, 1.062131147540984, 3.1613934426229497, 1101.0][2.0, 12.272352941176464, 1.9286764705882358, 2.2503676470588228, 20.209558823529413, 94.72058823529412, 2.229705882352941, 2.0449999999999995, 0.3635294117647058, 1.6292647058823533, 3.0122794117647054, 1.0586176470588233, 2.7795588235294093, 517.6102941176471][2.9693877551020407, 13.146530612244899, 3.3402040816326526, 2.428163265306121, 21.360204081632656, 98.74489795918367, 1.6831632653061233, 0.7961224489795918, 0.4522448979591836, 1.151734693877551, 7.34581630612245, 0.6867346938775512, 1.6943877551020412, 624.2551020408164]distance: 6.251918634057867E-4new clusters' centers:[1.0793650793650793, 13.648888888888889, 1.9598412698412708, 2.4379365079365085, 17.352380952380955, 105.95238095238095, 2.8777777777777778, 3.0209523809523806, 0.2849206349206349, 1.9023809523809527, 5.475396825396826, 1.0622222222222224, 3.164761904761905, 1087.2222222222222][2.0, 12.265384615384615, 1.9243076923076925, 2.256461538461538, 20.17846153846154, 94.9076923076923, 2.1978461538461547, 2.0058461538461545, 0.3633846153846154, 1.628153846153846, 2.931076923076924, 1.0611692307692309, 2.7732307692307687, 515.5384615384615][2.94, 13.1396, 3.3464000000000005, 2.4195999999999995, 21.305999999999997, 98.2, 1.6874, 0.8101999999999998, 0.45679999999999993, 1.15, 7.29739998, 0.6906000000000002, 1.7047999999999994, 618.84]distance: 6.566946236291001E-4new clusters' centers:[1.0793650793650793, 13.648888888888889, 1.9598412698412708, 2.4379365079365085, 17.352380952380955, 105.95238095238095, 2.8777777777777778, 3.0209523809523806, 0.2849206349206349, 1.9023809523809527, 5.475396825396826, 1.0622222222222224, 3.164761904761905, 1087.2222222222222][2.0, 12.265384615384615, 1.9243076923076925, 2.256461538461538, 20.17846153846154, 94.9076923076923, 2.1978461538461547, 2.0058461538461545, 0.3633846153846154, 1.628153846153846, 2.931076923076924, 1.0611692307692309, 2.7732307692307687, 515.5384615384615][2.94, 13.1396, 3.3464000000000005, 2.4195999999999995, 21.305999999999997, 98.2, 1.6874, 0.8101999999999998, 0.45679999999999993, 1.15, 7.29739998, 0.6906000000000002, 1.7047999999999994, 618.84]distance: 0.0Cluster: 1   size: 63 :[1.0, 14.23, 1.71, 2.43, 15.6, 127.0, 2.8, 3.06, 0.28, 2.29, 5.64, 1.04, 3.92, 1065.0][1.0, 13.2, 1.78, 2.14, 11.2, 100.0, 2.65, 2.76, 0.26, 1.28, 4.38, 1.05, 3.4, 1050.0][1.0, 13.16, 2.36, 2.67, 18.6, 101.0, 2.8, 3.24, 0.3, 2.81, 5.68, 1.03, 3.17, 1185.0][1.0, 14.37, 1.95, 2.5, 16.8, 113.0, 3.85, 3.49, 0.24, 2.18, 7.8, 0.86, 3.45, 1480.0][1.0, 13.24, 2.59, 2.87, 21.0, 118.0, 2.8, 2.69, 0.39, 1.82, 4.32, 1.04, 2.93, 735.0][1.0, 14.2, 1.76, 2.45, 15.2, 112.0, 3.27, 3.39, 0.34, 1.97, 6.75, 1.05, 2.85, 1450.0][1.0, 14.39, 1.87, 2.45, 14.6, 96.0, 2.5, 2.52, 0.3, 1.98, 5.25, 1.02, 3.58, 1290.0][1.0, 14.06, 2.15, 2.61, 17.6, 121.0, 2.6, 2.51, 0.31, 1.25, 5.05, 1.06, 3.58, 1295.0][1.0, 14.83, 1.64, 2.17, 14.0, 97.0, 2.8, 2.98, 0.29, 1.98, 5.2, 1.08, 2.85, 1045.0][1.0, 13.86, 1.35, 2.27, 16.0, 98.0, 2.98, 3.15, 0.22, 1.85, 7.22, 1.01, 3.55, 1045.0][1.0, 14.1, 2.16, 2.3, 18.0, 105.0, 2.95, 3.32, 0.22, 2.38, 5.75, 1.25, 3.17, 1510.0][1.0, 14.12, 1.48, 2.32, 16.8, 95.0, 2.2, 2.43, 0.26, 1.57, 5.0, 1.17, 2.82, 1280.0][1.0, 13.75, 1.73, 2.41, 16.0, 89.0, 2.6, 2.76, 0.29, 1.81, 5.6, 1.15, 2.9, 1320.0][1.0, 14.75, 1.73, 2.39, 11.4, 91.0, 3.1, 3.69, 0.43, 2.81, 5.4, 1.25, 2.73, 1150.0][1.0, 14.38, 1.87, 2.38, 12.0, 102.0, 3.3, 3.64, 0.29, 2.96, 7.5, 1.2, 3.0, 1547.0][1.0, 13.63, 1.81, 2.7, 17.2, 112.0, 2.85, 2.91, 0.3, 1.46, 7.3, 1.28, 2.88, 1310.0][1.0, 14.3, 1.92, 2.72, 20.0, 120.0, 2.8, 3.14, 0.33, 1.97, 6.2, 1.07, 2.65, 1280.0][1.0, 13.83, 1.57, 2.62, 20.0, 115.0, 2.95, 3.4, 0.4, 1.72, 6.6, 1.13, 2.57, 1130.0][1.0, 14.19, 1.59, 2.48, 16.5, 108.0, 3.3, 3.93, 0.32, 1.86, 8.7, 1.23, 2.82, 1680.0][1.0, 13.64, 3.1, 2.56, 15.2, 116.0, 2.7, 3.03, 0.17, 1.66, 5.1, 0.96, 3.36, 845.0][1.0, 14.06, 1.63, 2.28, 16.0, 126.0, 3.0, 3.17, 0.24, 2.1, 5.65, 1.09, 3.71, 780.0][1.0, 12.93, 3.8, 2.65, 18.6, 102.0, 2.41, 2.41, 0.25, 1.98, 4.5, 1.03, 3.52, 770.0][1.0, 13.71, 1.86, 2.36, 16.6, 101.0, 2.61, 2.88, 0.27, 1.69, 3.8, 1.11, 4.0, 1035.0][1.0, 12.85, 1.6, 2.52, 17.8, 95.0, 2.48, 2.37, 0.26, 1.46, 3.93, 1.09, 3.63, 1015.0][1.0, 13.5, 1.81, 2.61, 20.0, 96.0, 2.53, 2.61, 0.28, 1.66, 3.52, 1.12, 3.82, 845.0][1.0, 13.39, 1.77, 2.62, 16.1, 93.0, 2.85, 2.94, 0.34, 1.45, 4.8, 0.92, 3.22, 1195.0][1.0, 13.3, 1.72, 2.14, 17.0, 94.0, 2.4, 2.19, 0.27, 1.35, 3.95, 1.02, 2.77, 1285.0][1.0, 13.87, 1.9, 2.8, 19.4, 107.0, 2.95, 2.97, 0.37, 1.76, 4.5, 1.25, 3.4, 915.0][1.0, 14.02, 1.68, 2.21, 16.0, 96.0, 2.65, 2.33, 0.26, 1.98, 4.7, 1.04, 3.59, 1035.0][1.0, 13.73, 1.5, 2.7, 22.5, 101.0, 3.0, 3.25, 0.29, 2.38, 5.7, 1.19, 2.71, 1285.0][1.0, 13.58, 1.66, 2.36, 19.1, 106.0, 2.86, 3.19, 0.22, 1.95, 6.9, 1.09, 2.88, 1515.0][1.0, 13.68, 1.83, 2.36, 17.2, 104.0, 2.42, 2.69, 0.42, 1.97, 3.84, 1.23, 2.87, 990.0][1.0, 13.76, 1.53, 2.7, 19.5, 132.0, 2.95, 2.74, 0.5, 1.35, 5.4, 1.25, 3.0, 1235.0][1.0, 13.51, 1.8, 2.65, 19.0, 110.0, 2.35, 2.53, 0.29, 1.54, 4.2, 1.1, 2.87, 1095.0][1.0, 13.48, 1.81, 2.41, 20.5, 100.0, 2.7, 2.98, 0.26, 1.86, 5.1, 1.04, 3.47, 920.0][1.0, 13.28, 1.64, 2.84, 15.5, 110.0, 2.6, 2.68, 0.34, 1.36, 4.6, 1.09, 2.78, 880.0][1.0, 13.05, 1.65, 2.55, 18.0, 98.0, 2.45, 2.43, 0.29, 1.44, 4.25, 1.12, 2.51, 1105.0][1.0, 13.07, 1.5, 2.1, 15.5, 98.0, 2.4, 2.64, 0.28, 1.37, 3.7, 1.18, 2.69, 1020.0][1.0, 14.22, 3.99, 2.51, 13.2, 128.0, 3.0, 3.04, 0.2, 2.08, 5.1, 0.89, 3.53, 760.0][1.0, 13.56, 1.71, 2.31, 16.2, 117.0, 3.15, 3.29, 0.34, 2.34, 6.13, 0.95, 3.38, 795.0][1.0, 13.41, 3.84, 2.12, 18.8, 90.0, 2.45, 2.68, 0.27, 1.48, 4.28, 0.91, 3.0, 1035.0][1.0, 13.88, 1.89, 2.59, 15.0, 101.0, 3.25, 3.56, 0.17, 1.7, 5.43, 0.88, 3.56, 1095.0][1.0, 13.24, 3.98, 2.29, 17.5, 103.0, 2.64, 2.63, 0.32, 1.66, 4.36, 0.82, 3.0, 680.0][1.0, 13.05, 1.77, 2.1, 17.0, 107.0, 3.0, 3.0, 0.28, 2.03, 5.04, 0.88, 3.35, 885.0][1.0, 14.21, 4.04, 2.44, 18.9, 111.0, 2.85, 2.65, 0.3, 1.25, 5.24, 0.87, 3.33, 1080.0][1.0, 14.38, 3.59, 2.28, 16.0, 102.0, 3.25, 3.17, 0.27, 2.19, 4.9, 1.04, 3.44, 1065.0][1.0, 13.9, 1.68, 2.12, 16.0, 101.0, 3.1, 3.39, 0.21, 2.14, 6.1, 0.91, 3.33, 985.0][1.0, 14.1, 2.02, 2.4, 18.8, 103.0, 2.75, 2.92, 0.32, 2.38, 6.2, 1.07, 2.75, 1060.0][1.0, 13.94, 1.73, 2.27, 17.4, 108.0, 2.88, 3.54, 0.32, 2.08, 8.9, 1.12, 3.1, 1260.0][1.0, 13.05, 1.73, 2.04, 12.4, 92.0, 2.72, 3.27, 0.17, 2.91, 7.2, 1.12, 2.91, 1150.0][1.0, 13.83, 1.65, 2.6, 17.2, 94.0, 2.45, 2.99, 0.22, 2.29, 5.6, 1.24, 3.37, 1265.0][1.0, 13.82, 1.75, 2.42, 14.0, 111.0, 3.88, 3.74, 0.32, 1.87, 7.05, 1.01, 3.26, 1190.0][1.0, 13.77, 1.9, 2.68, 17.1, 115.0, 3.0, 2.79, 0.39, 1.68, 6.3, 1.13, 2.93, 1375.0][1.0, 13.74, 1.67, 2.25, 16.4, 118.0, 2.6, 2.9, 0.21, 1.62, 5.85, 0.92, 3.2, 1060.0][1.0, 13.56, 1.73, 2.46, 20.5, 116.0, 2.96, 2.78, 0.2, 2.45, 6.25, 0.98, 3.03, 1120.0][1.0, 14.22, 1.7, 2.3, 16.3, 118.0, 3.2, 3.0, 0.26, 2.03, 6.38, 0.94, 3.31, 970.0][1.0, 13.29, 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