1.项目背景 在做交通路线分析的时候，客户需要找出车辆的行车规律，我们将车辆每天的行车路线当做一个数据样本，总共有365天或是更多，从这些数据中通过聚类来获得行车路线规律统计分析。
我首先想到是K-means算法，不过它的算法思想是任选K个中心点，然后不停的迭代，在迭代的过程中需要不停的更新中心点。在我们着这个项目中，此方案不能解决，因为我们是通过编辑距离来计算两条路线的相似度。可以参考(1.交通聚类：编辑距离 (Levenshtein距离)Java实现) 这篇文章了解一下编辑距离。当我们第一步选出k个中心点后，并且两两计算编辑距离，然后再重新选择中心点，这时问题出来了，我们得到了编辑距离的均值，是数字化的。如果在根据这个均值两两比较编辑距离就无法实现了。故此方案废弃。
2.层次聚类概述(Hierarchical Clustering) 基于层次的聚类方法(系统聚类方法)：对给定的数据集进行层次分解，直到某种条件满足为止。
1.凝聚的层次聚类：自底向上，首先将每个对象作为一个族开始，每一步合并两个最近的簇，直到满足簇的数目。如AGNES算法。每一项自成一类；迭代，将最近的两类合并为一类。
2.分裂的层次聚类： 自顶向下，从包含所有对象的一个簇开始，每一步分裂一个簇，直到簇的数目。如DLANA算法。将所有项看做一类；找出最不相似的项分裂出去成为两类。 相信大家读完上边的陈述已经明白是怎么回事了。下边是代码，供以后用到的朋友学习研究。
3.代码： package agenes;
import java.util.ArrayList;
import java.util.List;
public class Cluster {
private List dataPoints = new ArrayList(); // 类簇中的样本点
private String clusterName;
public List getDataPoints() {
return dataPoints;
}
public void setDataPoints(List dataPoints) {
this.dataPoints = dataPoints;
}
public String getClusterName() {
return clusterName;
}
public void setClusterName(String clusterName) {
this.clusterName = clusterName;
}
}
package agenes;
import java.util.ArrayList;
import java.util.List;
public class ClusterAnalysis {
public List startAnalysis(List dataPoints,int ClusterNum){
List finalClusters=new ArrayList();
List originalClusters=initialCluster(dataPoints);
finalClusters=originalClusters;
while(finalClusters.size()>ClusterNum){
double min=Double.MAX_VALUE;
int mergeIndexA=0;
int mergeIndexB=0;
for(int i=0;i
for(int j=0;j
if(i!=j){
Cluster clusterA=finalClusters.get(i);
Cluster clusterB=finalClusters.get(j);
List dataPointsA=clusterA.getDataPoints();
List dataPointsB=clusterB.getDataPoints();
for(int m=0;m
for(int n=0;n
double tempDis=getDistance(dataPointsA.get(m),dataPointsB.get(n));
if(tempDis
min=tempDis;
mergeIndexA=i;
mergeIndexB=j;
}
}
}
}
} //end for j
}// end for i
//合并cluster[mergeIndexA]和cluster[mergeIndexB]
finalClusters=mergeCluster(finalClusters,mergeIndexA,mergeIndexB);
}//end while
return finalClusters;
}
private List mergeCluster(List clusters,int mergeIndexA,int mergeIndexB){
if (mergeIndexA != mergeIndexB) {
// 将cluster[mergeIndexB]中的DataPoint加入到 cluster[mergeIndexA]
Cluster clusterA = clusters.get(mergeIndexA);
Cluster clusterB = clusters.get(mergeIndexB);
List dpA = clusterA.getDataPoints();
List dpB = clusterB.getDataPoints();
for (DataPoint dp : dpB) {
DataPoint tempDp = new DataPoint();
// tempDp.setDataPointName(dp.getDataPointName());
// tempDp.setDimensioin(dp.getDimensioin());
// tempDp.setCluster(clusterA);
tempDp = dp;
tempDp.setCluster(clusterA);
dpA.add(tempDp);
}
clusterA.setDataPoints(dpA);
// List clusters中移除cluster[mergeIndexB]
clusters.remove(mergeIndexB);
}
return clusters;
}
// 初始化类簇
private List initialCluster(List dataPoints){
List originalClusters=new ArrayList();
for(int i=0;i
DataPoint tempDataPoint=dataPoints.get(i);
List tempDataPoints=new ArrayList();
tempDataPoints.add(tempDataPoint);
Cluster tempCluster=new Cluster();
tempCluster.setClusterName("Cluster "+String.valueOf(i));
tempCluster.setDataPoints(tempDataPoints);
tempDataPoint.setCluster(tempCluster);
originalClusters.add(tempCluster);
}
return originalClusters;
}
//计算两个样本点之间的欧几里得距离
private double getDistance(DataPoint dpA, DataPoint dpB){
double distance=0;
double[] dimA = dpA.getDimensioin();
double[] dimB = dpB.getDimensioin();
if (dimA.length == dimB.length) {
for (int i = 0; i < dimA.length; i++) {
double temp=Math.pow((dimA[i]-dimB[i]),2);
distance=distance+temp;
}
distance=Math.pow(distance, 0.5);
}
return distance;
}
public static void main(String[] args){
ArrayList dpoints = new ArrayList();
double[] a={2,3};
double[] b={2,4};
double[] c={1,4};
double[] d={1,3};
double[] e={2,2};
double[] f={3,2};
double[] g={8,7};
double[] h={8,6};
double[] i={7,7};
double[] j={7,6};
double[] k={8,5};
// double[] l={100,2};//孤立点
double[] m={8,20};
double[] n={8,19};
double[] o={7,18};
double[] p={7,17};
double[] q={8,20};
dpoints.add(new DataPoint(a,"a"));
dpoints.add(new DataPoint(b,"b"));
dpoints.add(new DataPoint(c,"c"));
dpoints.add(new DataPoint(d,"d"));
dpoints.add(new DataPoint(e,"e"));
dpoints.add(new DataPoint(f,"f"));
dpoints.add(new DataPoint(g,"g"));
dpoints.add(new DataPoint(h,"h"));
dpoints.add(new DataPoint(i,"i"));
dpoints.add(new DataPoint(j,"j"));
dpoints.add(new DataPoint(k,"k"));
// dataPoints.add(new DataPoint(l,"l"));
// dpoints.add(new DataPoint(l,"l"));
dpoints.add(new DataPoint(m,"m"));
dpoints.add(new DataPoint(n,"n"));
dpoints.add(new DataPoint(o,"o"));
dpoints.add(new DataPoint(p,"p"));
dpoints.add(new DataPoint(q,"q"));
int clusterNum=3; //类簇数
ClusterAnalysis ca=new ClusterAnalysis();
List clusters=ca.startAnalysis(dpoints, clusterNum);
for(Cluster cl:clusters){
System.out.println("------"+cl.getClusterName()+"------");
List tempDps=cl.getDataPoints();
for(DataPoint tempdp:tempDps){
System.out.println(tempdp.getDataPointName());
}
}
}
}
package agenes;
public class DataPoint {
String dataPointName; // 样本点名
Cluster cluster; // 样本点所属类簇
private double dimensioin[]; // 样本点的维度
public DataPoint() {
}
public DataPoint(double[] dimensioin, String dataPointName) {
this.dataPointName = dataPointName;
this.dimensioin = dimensioin;
}
public double[] getDimensioin() {
return dimensioin;
}
public void setDimensioin(double[] dimensioin) {
this.dimensioin = dimensioin;
}
public Cluster getCluster() {
return cluster;
}
public void setCluster(Cluster cluster) {
this.cluster = cluster;
}
public String getDataPointName() {
return dataPointName;
}
public void setDataPointName(String dataPointName) {
this.dataPointName = dataPointName;
}
}