上两篇文章分别用朴素贝叶斯算法和KNN算法对newgroup文本进行了分类測试。本文使用Kmeans算法对文本进行聚类。
1、文本预处理
文本预处理在前面两本文章中已经介绍，此处（略）。
2、文本向量化
package com.datamine.kmeans;
import java.io.*;
import java.util.*;
import java.util.Map.Entry;
/**
* 计算文档的属性向量，将全部文档向量化
* @author Administrator
*/
public class ComputeWordsVector {
/**
* 计算文档的TF-IDF属性向量。返回Map<文件名称，<特征词，TF-IDF值>>
* @param testSampleDir 处理好的聚类样本測试例子集
* @return 全部測试例子的属性向量构成的map
* @throws IOException
*/
public Map<String,Map<String,Double>> computeTFMultiIDF(String testSampleDir) throws IOException{
String word;
Map<String,Map<String,Double>> allTestSampleMap = new TreeMap<String, Map<String,Double>>();
Map<String,Double> idfPerWordMap = computeIDF(testSampleDir);
Map<String,Double> tfPerDocMap = new TreeMap<String, Double>();
File[] samples = new File(testSampleDir).listFiles();
System.out.println("the total number of test files is " + samples.length);
for(int i = 0;i<samples.length;i++){
tfPerDocMap.clear();
FileReader samReader = new FileReader(samples[i]);
BufferedReader samBR = new BufferedReader(samReader);
Double wordSumPerDoc = 0.0; //计算每篇文档的总词数
while((word = samBR.readLine()) != null){
if(!word.isEmpty()){
wordSumPerDoc++;
if(tfPerDocMap.containsKey(word))
tfPerDocMap.put(word, tfPerDocMap.get(word)+1.0);
else
tfPerDocMap.put(word, 1.0);
}
}
Double maxCount = 0.0,wordWeight; //记录出现次数最多的词的次数，用作归一化 ？？？
Set<Map.Entry<String, Double>> tempTF = tfPerDocMap.entrySet();
for(Iterator<Map.Entry<String, Double>> mt = tempTF.iterator();mt.hasNext();){
Map.Entry<String, Double> me = mt.next();
if(me.getValue() > maxCount)
maxCount = me.getValue();
}
for(Iterator<Map.Entry<String, Double>> mt = tempTF.iterator();mt.hasNext();){
Map.Entry<String, Double> me = mt.next();
Double IDF = Math.log(samples.length / idfPerWordMap.get(me.getKey()));
wordWeight = (me.getValue() / wordSumPerDoc) * IDF;
tfPerDocMap.put(me.getKey(), wordWeight);
}
TreeMap<String,Double> tempMap = new TreeMap<String, Double>();
tempMap.putAll(tfPerDocMap);
allTestSampleMap.put(samples[i].getName(), tempMap);
}
printTestSampleMap(allTestSampleMap);
return allTestSampleMap;
}
/**
* 输出測试例子map内容，用于測试
* @param allTestSampleMap
* @throws IOException
*/
private void printTestSampleMap(
Map<String, Map<String, Double>> allTestSampleMap) throws IOException {
// TODO Auto-generated method stub
File outPutFile = new File("E:/DataMiningSample/KmeansClusterResult/allTestSampleMap.txt");
FileWriter outPutFileWriter = new FileWriter(outPutFile);
Set<Map.Entry<String, Map<String,Double>>> allWords = allTestSampleMap.entrySet();
for(Iterator<Entry<String, Map<String, Double>>> it = allWords.iterator();it.hasNext();){
Map.Entry<String, Map<String,Double>> me = it.next();
outPutFileWriter.append(me.getKey()+" ");
Set<Map.Entry<String, Double>> vectorSet = me.getValue().entrySet();
for(Iterator<Map.Entry<String, Double>> vt = vectorSet.iterator();vt.hasNext();){
Map.Entry<String, Double> vme = vt.next();
outPutFileWriter.append(vme.getKey()+" "+vme.getValue()+" ");
}
outPutFileWriter.append("
");
outPutFileWriter.flush();
}
outPutFileWriter.close();
}
/**
* 统计每一个词的总出现次数，返回出现次数大于n次的词汇构成终于的属性词典
* @param strDir 处理好的newsgroup文件文件夹的绝对路径
* @param wordMap 记录出现的每一个词构成的属性词典
* @return newWordMap 返回出现次数大于n次的词汇构成终于的属性词典
* @throws IOException
*/
public SortedMap<String, Double> countWords(String strDir,
Map<String, Double> wordMap) throws IOException {
File sampleFile = new File(strDir);
File[] sample = sampleFile.listFiles();
String word;
for(int i =0 ;i < sample.length;i++){
if(!sample[i].isDirectory()){
FileReader samReader = new FileReader(sample[i]);
BufferedReader samBR = new BufferedReader(samReader);
while((word = samBR.readLine()) != null){
if(!word.isEmpty() && wordMap.containsKey(word))
wordMap.put(word, wordMap.get(word)+1);
else
wordMap.put(word, 1.0);
}
samBR.close();
}else{
countWords(sample[i].getCanonicalPath(),wordMap);
}
}
/*
* 去除停顿词后。先用DF算法选取特征词，后面再增加特征词的选取算法
*/
SortedMap<String,Double> newWordMap = new TreeMap<String, Double>();
Set<Map.Entry<String, Double>> allWords = wordMap.entrySet();
for(Iterator<Map.Entry<String, Double>> it = allWords.iterator();it.hasNext();){
Map.Entry<String, Double> me = it.next();
if(me.getValue() > 100) //DF算法降维
newWordMap.put(me.getKey(), me.getValue());
}
return newWordMap;
}
/**
* 计算IDF，即属性词典中每一个词在多少个文档中出现过
* @param testSampleDir 聚类算法測试样本所在的文件夹
* @return 单词IDFmap <单词，包括该单词的文档数>
* @throws IOException
*/
public Map<String,Double> computeIDF(String testSampleDir) throws IOException{
Map<String,Double> IDFPerWordMap = new TreeMap<String, Double>();
//记下当前已经遇到过的该文档中的词
Set<String> alreadyCountWord = new HashSet<String>();
String word;
File[] samples = new File(testSampleDir).listFiles();
for(int i = 0;i<samples.length;i++){
alreadyCountWord.clear();
FileReader tsReader = new FileReader(samples[i]);
BufferedReader tsBR = new BufferedReader(tsReader);
while((word = tsBR.readLine()) != null){
if(!alreadyCountWord.contains(word)){
if(IDFPerWordMap.containsKey(word))
IDFPerWordMap.put(word, IDFPerWordMap.get(word)+1.0);
else
IDFPerWordMap.put(word, 1.0);
alreadyCountWord.add(word);
}
}
}
return IDFPerWordMap;
}
/**
* 创建聚类算法的測试例子集。主要是过滤出仅仅含有特征词的文档写到一个文件夹下
* @param srcDir 源文件夹，已经预处理可是还没有过滤非特征词的文档文件夹
* @param desDir 目的文件夹，聚类算法的測试例子文件夹
* @return 创建測试例子集中特征词数组
* @throws IOException
*/
public String[] createTestSamples(String srcDir, String desDir) throws IOException {
SortedMap<String,Double> wordMap = new TreeMap<String, Double>();
wordMap = countWords(srcDir,wordMap);
System.out.println("special words map sizes:" + wordMap.size());
String word,testSampleFile;
File[] sampleDir = new File(srcDir).listFiles();
for(int i =0;i<sampleDir.length;i++){
File[] sample = sampleDir[i].listFiles();
for(int j =0;j<sample.length;j++){
testSampleFile = desDir + sampleDir[i].getName()+"_"+sample[j].getName();
FileReader samReader = new FileReader(sample[j]);
BufferedReader samBR = new BufferedReader(samReader);
FileWriter tsWriter = new FileWriter(new File(testSampleFile));
while((word = samBR.readLine()) != null){
if(wordMap.containsKey(word))
tsWriter.append(word + "
");
}
tsWriter.flush();
tsWriter.close();
}
}
//返回属性词典
String[] terms = new String[wordMap.size()];
int i = 0;
Set<Map.Entry<String, Double>> allWords = wordMap.entrySet();
for(Iterator<Map.Entry<String, Double>> it = allWords.iterator();it.hasNext();){
Map.Entry<String, Double> me = it.next();
terms[i] = me.getKey();
i++;
}
return terms;
}
}
3、Kmeans算法
Kmeans算法是很经典的聚类算法，算法主要过程例如以下：先选K个（或者随机选择）初始聚类点作为初始中心点，然后就算其它全部点到K个聚类中心点的距离，将点分到近期的聚类中。聚类完后。再次计算各个类中的中心点，中心点发生变化，于是更新中心点，然后再计算其它点到中心点的距离又一次聚类。中心点又发生变化，如此迭代下去。
初始点选取策略：随机选。均匀抽样，最大最小法等....
距离的度量方法：1-余弦相似度，2-向量内积
算法停止条件：计算准则函数及设置最大迭代次数
空聚类的处理：注意空聚类导致的程序bug
package com.datamine.kmeans;
import java.io.BufferedReader;
import java.io.FileNotFoundException;
import java.io.FileReader;
import java.io.FileWriter;
import java.io.IOException;
import java.util.*;
/**
* kmeans聚类算法的实现类，将newsgroup文档集聚成10类、20类、30类
* 算法结束条件：当每一个点近期的聚类中心点就是它所属的聚类中心点时。算法结束
* @author Administrator
*
*/
public class KmeansCluster {
/**
* kmeans算法主过程
* @param allTestSampleMap 聚类算法測试样本map(已经向量化) <文件名称，<特征词，TF-IDF值>>
* @param k 聚类的数量
* @return 聚类结果 <文件名称，聚类完毕后所属的类别号>
*/
private Map<String, Integer> doProcess(
Map<String, Map<String, Double>> allTestSampleMap, int k) {
//0、首先获取allTestSampleMap全部文件名称顺序组成的数组
String[] testSampleNames = new String[allTestSampleMap.size()];
int count =0,tsLength = allTestSampleMap.size();
Set<Map.Entry<String, Map<String,Double>>> allTestSampleMapSet = allTestSampleMap.entrySet();
for(Iterator<Map.Entry<String, Map<String,Double>>> it = allTestSampleMapSet.iterator();it.hasNext();){
Map.Entry<String, Map<String,Double>> me = it.next();
testSampleNames[count++] = me.getKey();
}
//1、初始点的选择算法是随机选择或者是均匀分开选择。这里採用后者
Map<Integer,Map<String,Double>> meansMap = getInitPoint(allTestSampleMap,k);
double [][] distance = new double[tsLength][k]; //distance[i][k]记录点i到聚类中心k的距离
//2、初始化k个聚类
int[] assignMeans = new int[tsLength]; //记录全部点属于的聚类序号，初始化全部为0
Map<Integer,Vector<Integer>> clusterMember = new TreeMap<Integer, Vector<Integer>>();//记录每一个聚类的成员点序号
Vector<Integer> mem = new Vector<Integer>();
int iterNum = 0; //迭代次数
while(true){
System.out.println("Iteration No." + (iterNum++) + "-------------------------");
//3、计算每一个点和每一个聚类中心的距离
for(int i = 0;i < tsLength;i++){
for(int j = 0;j<k;j++)
distance[i][j] = getDistance(allTestSampleMap.get(testSampleNames[i]),meansMap.get(j));
}
//4、找出每一个点近期的聚类中心
int [] nearestMeans = new int[tsLength];
for(int i = 0;i < tsLength;i++){
nearestMeans[i] = findNearestMeans(distance,i);
}
//5、推断当前全部点属于的聚类序号是否已经全部是其离的近期的聚类，假设是或者达到最大的迭代次数。那么结束算法
int okCount = 0;
for(int i= 0;i<tsLength;i++){
if(nearestMeans[i] == assignMeans[i])
okCount ++;
}
System.out.println("okCount = " + okCount);
if(okCount == tsLength || iterNum >= 10)
break;
//6、假设前面条件不满足，那么须要又一次聚类再次进行一次迭代，须要改动每一个聚类的成员和每一个点属于的聚类信息
clusterMember.clear();
for(int i = 0;i < tsLength;i++){
assignMeans[i] = nearestMeans[i];
if(clusterMember.containsKey(nearestMeans[i])){
clusterMember.get(nearestMeans[i]).add(i);
}
else{
mem.clear();
mem.add(i);
Vector<Integer> tempMem = new Vector<Integer>();
tempMem.addAll(mem);
clusterMember.put(nearestMeans[i], tempMem);
}
}
//7、又一次计算每一个聚类的中心点
for(int i = 0;i<k;i++){
if(!clusterMember.containsKey(i)) //注意kmeans可能产生空聚类
continue;
Map<String,Double> newMean = computeNewMean(clusterMember.get(i),allTestSampleMap,testSampleNames);
Map<String,Double> tempMean = new TreeMap<String,Double>();
tempMean.putAll(newMean);
meansMap.put(i, tempMean);
}
}
//8、形成聚类结果而且返回
Map<String,Integer> resMap = new TreeMap<String,Integer>();
for(int i = 0;i<tsLength;i++){
resMap.put(testSampleNames[i], assignMeans[i]);
}
return resMap;
}
/**
* 计算当前聚类的新中心，採用向量平均
* @param clusterM 该点到全部聚类中心的距离
* @param allTestSampleMap 全部測试例子 <文件名称，向量>
* @param testSampleNames 全部測试例子名构成的数组
* @return 新的聚类中心向量
*/
private Map<String, Double> computeNewMean(Vector<Integer> clusterM,
Map<String, Map<String, Double>> allTestSampleMap,
String[] testSampleNames) {
double memberNum = (double)clusterM.size();
Map<String,Double> newMeanMap = new TreeMap<String,Double>();
Map<String,Double> currentMemMap = new TreeMap<String, Double>();
for(Iterator<Integer> it = clusterM.iterator();it.hasNext();){
int me = it.next();
currentMemMap = allTestSampleMap.get(testSampleNames[me]);
Set<Map.Entry<String, Double>> currentMemMapSet = currentMemMap.entrySet();
for(Iterator<Map.Entry<String, Double>> jt = currentMemMapSet.iterator();jt.hasNext();){
Map.Entry<String, Double> ne = jt.next();
if(newMeanMap.containsKey(ne.getKey()))
newMeanMap.put(ne.getKey(), newMeanMap.get(ne.getKey())+ne.getValue());
else
newMeanMap.put(ne.getKey(), ne.getValue());
}
}
Set<Map.Entry<String, Double>> newMeanMapSet = newMeanMap.entrySet();
for(Iterator<Map.Entry<String, Double>> it = newMeanMapSet.iterator();it.hasNext();){
Map.Entry<String, Double> me = it.next();
newMeanMap.put(me.getKey(), newMeanMap.get(me.getKey()) / memberNum);
}
return newMeanMap;
}
/**
* 找出距离当前点近期的聚类中心
* @param distance 点到全部聚类中心的距离
* @param m 点（文本号）
* @return 近期聚类中心的序号j
*/
private int findNearestMeans(double[][] distance, int m) {
double minDist = 10;
int j = 0;
for(int i = 0;i<distance[m].length;i++){
if(distance[m][i] < minDist){
minDist = distance[m][i];
j = i;
}
}
return j;
}
/**
* 计算两个点的距离
* @param map1 点1的向量map
* @param map2 点2的向量map
* @return 两个点的欧式距离
*/
private double getDistance(Map<String, Double> map1, Map<String, Double> map2) {
return 1 - computeSim(map1,map2);
}
/**计算两个文本的类似度
* @param testWordTFMap 文本1的<单词,词频>向量
* @param trainWordTFMap 文本2<单词,词频>向量
* @return Double 向量之间的类似度 以向量夹角余弦计算（加上凝视部分代码就可以）或者向量内积计算（不加凝视部分，效果相当而速度更快）
* @throws IOException
*/
private double computeSim(Map<String, Double> testWordTFMap,
Map<String, Double> trainWordTFMap) {
// TODO Auto-generated method stub
double mul = 0;//, testAbs = 0, trainAbs = 0;
Set<Map.Entry<String, Double>> testWordTFMapSet = testWordTFMap.entrySet();
for(Iterator<Map.Entry<String, Double>> it = testWordTFMapSet.iterator(); it.hasNext();){
Map.Entry<String, Double> me = it.next();
if(trainWordTFMap.containsKey(me.getKey())){
mul += me.getValue()*trainWordTFMap.get(me.getKey());
}
//testAbs += me.getValue() * me.getValue();
}
//testAbs = Math.sqrt(testAbs);
/*Set<Map.Entry<String, Double>> trainWordTFMapSet = trainWordTFMap.entrySet();
for(Iterator<Map.Entry<String, Double>> it = trainWordTFMapSet.iterator(); it.hasNext();){
Map.Entry<String, Double> me = it.next();
trainAbs += me.getValue()*me.getValue();
}
trainAbs = Math.sqrt(trainAbs);*/
return mul ;/// (testAbs * trainAbs);
}
/**
* 获取kmeans算法迭代的初始点
* @param allTestSampleMap <文件名称，<特征词。TF-IDF值>>
* @param k 聚类的数量
* @return meansMap k个聚类的中心点向量
*/
private Map<Integer, Map<String, Double>> getInitPoint(
Map<String, Map<String, Double>> allTestSampleMap, int k) {
int count = 0, i = 0;
//保存k个聚类的中心向量
Map<Integer,Map<String,Double>> meansMap = new TreeMap<Integer, Map<String,Double>>();
System.out.println("本次聚类的初始点相应的文件为：");
Set<Map.Entry<String, Map<String,Double>>> allTestSampleMapSet = allTestSampleMap.entrySet();
for(Iterator<Map.Entry<String, Map<String,Double>>> it = allTestSampleMapSet.iterator();it.hasNext();){
Map.Entry<String, Map<String,Double>> me = it.next();
if(count == i*allTestSampleMapSet.size() / k){
meansMap.put(i, me.getValue());
System.out.println(me.getKey());
i++;
}
count++ ;
}
return meansMap;
}
/**
* 输出聚类结果到文件里
* @param kmeansClusterResult 聚类结果
* @param kmeansClusterResultFile 输出聚类结果到文件里
* @throws IOException
*/
private void printClusterResult(Map<String, Integer> kmeansClusterResult,
String kmeansClusterResultFile) throws IOException {
FileWriter resultWriter = new FileWriter(kmeansClusterResultFile);
Set<Map.Entry<String, Integer>> kmeansClusterResultSet = kmeansClusterResult.entrySet();
for(Iterator<Map.Entry<String, Integer>> it = kmeansClusterResultSet.iterator();it.hasNext();){
Map.Entry<String, Integer> me = it.next();
resultWriter.append(me.getKey()+" "+me.getValue()+"
");
}
resultWriter.flush();
resultWriter.close();
}
/**
* 评估函数依据聚类结果文件统计熵 和 混淆矩阵
* @param kmeansClusterResultFile 聚类结果文件
* @param k 聚类数目
* @return 聚类结果的熵值
* @throws IOException
*/
private double evaluateClusterResult(String kmeansClusterResultFile, int k) throws IOException {
Map<String,String> rightCate = new TreeMap<String, String>();
Map<String,String> resultCate = new TreeMap<String, String>();
FileReader crReader = new FileReader(kmeansClusterResultFile);
BufferedReader crBR = new BufferedReader(crReader);
String[] s;
String line;
while((line = crBR.readLine()) != null){
s = line.split(" ");
resultCate.put(s[0], s[1]);
rightCate.put(s[0], s[0].split("_")[0]);
}
crBR.close();
return computeEntropyAndConfuMatrix(rightCate,resultCate,k);//返回熵
}
/**
* 计算混淆矩阵并输出，返回熵
* @param rightCate 正确的类目相应map
* @param resultCate 聚类结果相应map
* @param k 聚类的数目
* @return 返回聚类熵
*/
private double computeEntropyAndConfuMatrix(Map<String, String> rightCate,
Map<String, String> resultCate, int k) {
//k行20列，[i,j]表示聚类i中属于类目j的文件数
int[][] confusionMatrix = new int[k][20];
//首先求出类目相应的数组索引
SortedSet<String> cateNames = new TreeSet<String>();
Set<Map.Entry<String, String>> rightCateSet = rightCate.entrySet();
for(Iterator<Map.Entry<String, String>> it = rightCateSet.iterator();it.hasNext();){
Map.Entry<String, String> me = it.next();
cateNames.add(me.getValue());
}
String[] cateNamesArray = cateNames.toArray(new String[0]);
Map<String,Integer> cateNamesToIndex = new TreeMap<String, Integer>();
for(int i =0;i < cateNamesArray.length ;i++){
cateNamesToIndex.put(cateNamesArray[i], i);
}
for(Iterator<Map.Entry<String, String>> it = rightCateSet.iterator();it.hasNext();){
Map.Entry<String, String> me = it.next();
confusionMatrix[Integer.parseInt(resultCate.get(me.getKey()))][cateNamesToIndex.get(me.getValue())]++;
}
//输出混淆矩阵
double [] clusterSum = new double[k]; //记录每一个聚类的文件数
double [] everyClusterEntropy = new double[k]; //记录每一个聚类的熵
double clusterEntropy = 0;
System.out.print(" ");
for(int i=0;i<20;i++){
System.out.printf("%-6d",i);
}
System.out.println();
for(int i =0;i<k;i++){
System.out.printf("%-6d",i);
for(int j = 0;j<20;j++){
clusterSum[i] += confusionMatrix[i][j];
System.out.printf("%-6d",confusionMatrix[i][j]);
}
System.out.println();
}
System.out.println();
//计算熵值
for(int i = 0;i<k;i++){
if(clusterSum[i] != 0){
for(int j = 0;j< 20 ;j++){
double p = (double)confusionMatrix[i][j]/clusterSum[i];
if(p!=0)
everyClusterEntropy[i] += -p * Math.log(p);
}
clusterEntropy += clusterSum[i]/(double)rightCate.size() * everyClusterEntropy[i];
}
}
return clusterEntropy;
}
public void KmeansClusterMain(String testSampleDir) throws IOException {
//首先计算文档TF-IDF向量，保存为Map<String,Map<String,Double>> 即为Map<文件名称,Map<特征词，TF-IDF值>>
ComputeWordsVector computV = new ComputeWordsVector();
//int k[] = {10,20,30}; 三组分类
int k[] = {20};
Map<String,Map<String,Double>> allTestSampleMap = computV.computeTFMultiIDF(testSampleDir);
for(int i =0;i<k.length;i++){
System.out.println("開始聚类。聚成"+k[i]+"类");
String KmeansClusterResultFile = "E:\DataMiningSample\KmeansClusterResult\";
Map<String,Integer> KmeansClusterResult = new TreeMap<String, Integer>();
KmeansClusterResult = doProcess(allTestSampleMap,k[i]);
KmeansClusterResultFile += k[i];
printClusterResult(KmeansClusterResult,KmeansClusterResultFile);
System.out.println("The Entropy for this Cluster is " + evaluateClusterResult(KmeansClusterResultFile,k[i]));
}
}
public static void main(String[] args) throws IOException {
KmeansCluster test = new KmeansCluster();
String KmeansClusterResultFile = "E:\DataMiningSample\KmeansClusterResult\20";
System.out.println("The Entropy for this Cluster is " + test.evaluateClusterResult(KmeansClusterResultFile,20));
}
}
4、程序入口
package com.datamine.kmeans;
import java.io.IOException;
import java.text.SimpleDateFormat;
import java.util.Date;
public class ClusterMain {
/**
* Kmeans 聚类主程序入口
* @param args
* @throws IOException
*/
public static void main(String[] args) throws IOException {
//数据预处理 在分类算法中已经实现 这里（略）
ComputeWordsVector computeV = new ComputeWordsVector();
KmeansCluster kmeansCluster = new KmeansCluster();
String srcDir = "E:\DataMiningSample\processedSample\";
String desDir = "E:\DataMiningSample\clusterTestSample\";
SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
String beginTime = sdf.format(new Date());
System.out.println("程序開始运行时间："+beginTime);
String[] terms = computeV.createTestSamples(srcDir,desDir);
kmeansCluster.KmeansClusterMain(desDir);
String endTime = sdf.format(new Date());
System.out.println("程序结束运行时间："+endTime);
}
}
5、聚类结果
程序開始运行时间：2016-03-14 17:02:38
special words map sizes:3832
the total number of test files is 18828
開始聚类，聚成20类
本次聚类的初始点相应的文件为：
alt.atheism_49960
comp.graphics_38307
comp.os.ms-windows.misc_10112
comp.sys.ibm.pc.hardware_58990
comp.sys.mac.hardware_50449
comp.windows.x_66402
comp.windows.x_68299
misc.forsale_76828
rec.autos_103685
rec.motorcycles_105046
rec.sport.baseball_104941
rec.sport.hockey_54126
sci.crypt_15819
sci.electronics_54016
sci.med_59222
sci.space_61185
soc.religion.christian_20966
talk.politics.guns_54517
talk.politics.mideast_76331
talk.politics.misc_178699
Iteration No.0-------------------------
okCount = 512
Iteration No.1-------------------------
okCount = 10372
Iteration No.2-------------------------
okCount = 15295
Iteration No.3-------------------------
okCount = 17033
Iteration No.4-------------------------
okCount = 17643
Iteration No.5-------------------------
okCount = 18052
Iteration No.6-------------------------
okCount = 18282
Iteration No.7-------------------------
okCount = 18404
Iteration No.8-------------------------
okCount = 18500
Iteration No.9-------------------------
okCount = 18627
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
0 482 0 3 3 1 1 0 5 2 1 0 0 2 27 11 53 4 6 15 176
1 4 601 69 8 14 127 7 5 5 8 0 14 31 16 34 2 2 2 1 5
2 1 64 661 96 18 257 26 9 3 0 0 13 25 13 6 2 3 2 6 2
3 0 56 78 575 213 15 119 15 6 2 1 4 131 2 4 2 6 0 2 1
4 1 25 13 151 563 11 50 3 3 1 2 14 125 4 8 1 0 3 0 0
5 2 28 78 25 37 348 13 2 0 0 2 5 38 5 6 2 1 1 2 8
6 20 80 24 21 23 166 38 45 45 26 10 37 87 34 27 22 15 8 35 12
7 4 20 6 24 45 6 629 28 20 14 0 3 87 10 4 1 8 0 13 0
8 0 2 1 10 8 4 25 781 40 1 1 0 70 5 10 2 8 4 2 3
9 4 2 11 0 1 1 11 34 831 1 0 1 7 7 0 1 1 1 8 0
10 10 7 6 2 4 1 7 7 4 633 4 5 11 18 9 5 13 8 10 3
11 1 0 1 9 4 1 20 1 3 286 961 0 17 8 4 2 2 0 5 3
12 3 14 0 6 1 2 2 0 1 1 0 858 51 1 1 2 16 8 69 4
13 3 15 4 7 7 17 5 12 8 5 2 5 46 13 793 6 5 2 30 5
14 2 4 0 1 0 2 4 6 3 4 4 2 14 746 3 1 2 3 55 11
15 30 43 29 39 15 18 12 13 7 3 4 13 195 38 36 5 6 18 5 11
16 195 1 0 2 0 1 1 0 4 1 4 1 4 16 6 846 3 6 16 274
17 8 2 0 2 4 2 1 5 7 0 0 10 30 12 5 28 363 9 289 23
18 19 1 0 0 2 0 0 6 0 1 1 3 1 3 2 9 8 843 48 18
19 10 8 1 1 1 0 2 13 2 6 3 3 9 12 18 5 444 16 164 69
The Entropy for this Cluster is 1.2444339205006887
程序结束运行时间：2016-03-14 17:08:24