2.交通聚类 -层次聚类（agnes）Java实现

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; /**
 * Created by zzy on 15/11/15.
 */

import java.util.ArrayList;
import java.util.List;

public class Cluster {
    private List<DataPoint> dataPoints = new ArrayList<DataPoint>(); // 类簇中的样本点
    private String clusterName;

    public List<DataPoint> getDataPoints() {
        return dataPoints;
    }

    public void setDataPoints(List<DataPoint> dataPoints) {
        this.dataPoints = dataPoints;
    }

    public String getClusterName() {
        return clusterName;
    }

    public void setClusterName(String clusterName) {
        this.clusterName = clusterName;
    }

}

package agenes; /**
 * Created by zzy on 15/11/15.
 */

import java.util.ArrayList;
import java.util.List;

public class ClusterAnalysis {

    public List<Cluster> startAnalysis(List<DataPoint> dataPoints,int ClusterNum){
        List<Cluster> finalClusters=new ArrayList<Cluster>();

        List<Cluster> 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<finalClusters.size();i++){
                for(int j=0;j<finalClusters.size();j++){
                    if(i!=j){
                        Cluster clusterA=finalClusters.get(i);
                        Cluster clusterB=finalClusters.get(j);

                        List<DataPoint> dataPointsA=clusterA.getDataPoints();
                        List<DataPoint> dataPointsB=clusterB.getDataPoints();

                        for(int m=0;m<dataPointsA.size();m++){
                            for(int n=0;n<dataPointsB.size();n++){
                                double tempDis=getDistance(dataPointsA.get(m),dataPointsB.get(n));
                                if(tempDis<min){
                                    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<Cluster> mergeCluster(List<Cluster> 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<DataPoint> dpA = clusterA.getDataPoints();
            List<DataPoint> 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<Cluster> clusters中移除cluster[mergeIndexB]
            clusters.remove(mergeIndexB);
        }

        return clusters;
    }

    // 初始化类簇
    private List<Cluster> initialCluster(List<DataPoint> dataPoints){
        List<Cluster> originalClusters=new ArrayList<Cluster>();
        for(int i=0;i<dataPoints.size();i++){
            DataPoint tempDataPoint=dataPoints.get(i);
            List<DataPoint> tempDataPoints=new ArrayList<DataPoint>();
            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<DataPoint> dpoints = new ArrayList<DataPoint>();

        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<Cluster> clusters=ca.startAnalysis(dpoints, clusterNum);

        for(Cluster cl:clusters){
            System.out.println("------"+cl.getClusterName()+"------");
            List<DataPoint> tempDps=cl.getDataPoints();
            for(DataPoint tempdp:tempDps){
                System.out.println(tempdp.getDataPointName());
            }
        }

    }
}

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package agenes;

/**
 * Created by zzy on 15/11/15.
 */

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;
    }
}

源码github:https://github.com/chaoren399/dkdemo/tree/master/agenes/src/agenes

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