k-中心点法的聚类数据挖掘算法的C#实现

最新推荐文章于 2023-05-21 22:54:49 发布

iteye_10078

最新推荐文章于 2023-05-21 22:54:49 发布

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文章标签：数据挖掘算法 C C++ C#

本文介绍了一种基于随机选择中心点的聚类算法实现。该算法通过不断迭代，将数据集中的对象分配给最近的中心点所代表的簇，并通过评估随机非中心点来优化簇中心，以提高聚类效果。

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using System;
using System.Collections.Generic;
using System.Text;
using System.Collections;
using System.Data.Odbc;

class ClusterGenerator : IDataWorker
{

#region IDataWorker Members

    public void work()
    {
        throw new Exception("The method or operation is not implemented.");
    }

#endregion

private int k = 10;

    public int K
    {
        get { return k; }
        set { k = value; }
    }

private ArrayList centerList = new ArrayList();

private ArrayList pointClusterList = new ArrayList();

    private ArrayList cluster = new ArrayList();
    private bool isChanged = false;
    private int minCenter = 0;
    private double minPower = 0;
    private double tempPower = 1;

private double sumPower = 0;
private double randPower = 0;

private int oRandom = 0;

    private void genCluster(ArrayList alldata)
    {
        centerList.Clear();
        //初始化clusterList

        Random random = new Random();
        for (int i = 0; i < k; i++)
        {
            centerList.Add(random.Next(alldata.Count));
            pointClusterList.Add(new ArrayList());
        }

        do
        {
            isChanged = false;
            /////////////////////////////指派每个剩余的对象给离它最近的中心点所代表的簇
            for (int i = 0; i < alldata.Count; i++)
            {

for (int j = 0; j < centerList.Count; j++)
{

tempPower = PowerUtil.countPower(alldata[i], alldata[(int)centerList[j]]);

                    if (tempPower < minPower)
                    {
                        minCenter = j;
                        minPower = tempPower;
                    }

                }
                cluster = (ArrayList)(pointClusterList[minCenter]);
                cluster.Add(i);

            }
            ///////////////////////////////////////////////////
            ////////////////////针对每个簇提出一个随机的非中心点对象
            //for (int i = 0; i < pointClusterList; i++) {
            //    cluster = (ArrayList)pointClusterList[i];

// oRandom = random.Next(alldata.Count);

// for (int j = 0; j < cluster; j++) {

// sumPower += PowerUtil.countPower(alldata((int)(cluster[j])), alldata[(int)centerList[i]]);

// randPower += PowerUtil.countPower(alldata((int)(cluster[j])), alldata(oRandom));
// }

// if (randPower < sumPower)
// {

// centerList[i] = oRandom;
// isChanged = true;

// }

            //}
            /////////////////////////
            for (int i = 0; i < pointClusterList; i++)
            {

oRandom = random.Next(alldata.Count);

if (PowerUtil.countEM(alldata, centerList, pointClusterList, oRandom, i))
{

                    centerList[i] = oRandom;
                    isChanged = true;
                    break;//?

}

///////////////////////////

/////////////////////////////////

} while (!isChanged);

}

}
using System;
using System.Collections.Generic;
using System.Text;
using System.Collections;

    class PowerUtil
    {
        public static double countPower(ArrayList a,ArrayList b) {

            double powerA=1.0;
            double powerB=1.0;
            double power=0;

int lengthA = a.Count;
int lengthB = b.Count;

for(int i=0;i for(int j=0;j

if (((string)a[i]).Equals((string)b[i]) && !((string)a[i]).Equals(""))
{

                       powerA = powerA - 1 / lengthA;
                   }
                   else {
                   if(((string)a[i]).Equals("")){

                       lengthA--;

                   }
                   }

               }

            }

for(int i=0;i for(int j=0;j

if (((string)b[i]).Equals((string)a[i]) && !((string)a[i]).Equals(""))
{

                       powerB = powerB - 1 / lengthB;
                   }
                   else {
                       if (((string)b[i]).Equals(""))
                       {

lengthB--;

                       }


                   }

               }

            }

return (powerA + powerB) / 2;

}

        public static bool countEM(ArrayList alldata, ArrayList centerList, ArrayList pointClusterList,int oRandom,int position)
        {

      int minCenter = 0;
      double minPower = 0;
      double tempPower = 1;
      double sumPower = 0;
      double randPower = 0;
        ArrayList cluster = new ArrayList();

            ArrayList newCenterList = centerList.Clone();
            newCenterList[position] = oRandom;
            ArrayList newPointClusterList = new ArrayList();
            for (int i = 0; i < pointClusterList.Count; i++)
            {

                newPointClusterList.Add(new ArrayList());
            }
            for (int i = 0; i < alldata.Count; i++)
            {

for (int j = 0; j < centerList.Count; j++)
{

tempPower = PowerUtil.countPower(alldata[i], alldata[(int)newCenterList[j]]);

                    if (tempPower < minPower)
                    {
                        minCenter = j;
                        minPower = tempPower;
                    }

                }
                cluster = (ArrayList)(newPointClusterList[minCenter]);
                cluster.Add(i);

}

//////////////////////////////
//开始算EM

            for (int i = 0; i < pointClusterList; i++)
            {
                cluster = (ArrayList)pointClusterList[i];

for (int j = 0; j < cluster; j++)
{

sumPower += PowerUtil.countPower(alldata((int)(cluster[j])), alldata[(int)centerList[i]]);

}

            }
            for (int i = 0; i < pointClusterList; i++)
            {
                cluster = (ArrayList)newPointClusterList[i];

for (int j = 0; j < cluster; j++)
{

randPower += PowerUtil.countPower(alldata((int)(cluster[j])), alldata[(int)newCenterList[i]]);

}

            double s=randPower-sumPower;
            if (s < 0)
            {
                return true;