基于Kmeans算法的文档聚类(包含Java代码及数据格式)

本文作者:合肥工业大学 管理学院 钱洋 email:1563178220@qq.com 内容可能有不到之处,欢迎交流。

未经本人允许禁止转载。

介绍

给定多篇文档,如何对文档进行聚类。本博客使用的是k-means聚类方法。关于k-means网络上有很多资料介绍其算法思想和其数学公式。

针对文档聚类,首先要讲文档进行向量化,也就是说要对文档进行编码。可以使用one-hot编码,也可以使用TF-IDF编码,也可以使用doc2vec编码等,总之,要将其向量化。

本人最近做文本分类时,使用的一个baseline就是k-means文档聚类。其借鉴的源码地址为: https://github.com/Hazoom/documents-k-means

在该源码基础上做了改进。

输入数据结构


基于Kmeans算法的文档聚类(包含Java代码及数据格式)

该输入文本的第一列为文本的标题,第二列是经过去高频词、停用词、低频词之后的数据。

源码

首先,我修改的是文档的表示,因为我的数据和作者的json数据并不同。

package com.clustering;

import java.io.BufferedReader;
import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Iterator;
import java.util.List;
import java.util.StringTokenizer;

/** Class for storing a collection of documents to be clustered. */
public class DocumentList implements Iterable<Document> {
    private final List<Document> documents = new ArrayList<Document>();
    private int numFeatures;

    /** Construct an empty DocumentList. */
    public DocumentList() {
    }

    /**
     * Construct a DocumentList by parsing the input string. The input string may contain multiple
     * document records. Each record must be delimited by curly braces {}.
     */
    /*public DocumentList(String input) {
        StringTokenizer st = new StringTokenizer(input, "{");
        int numDocuments = st.countTokens() - 1;
        String record = st.nextToken(); // skip empty split to left of {
        for (int i = 0; i < numDocuments; i++) {
            record = st.nextToken();
            Document document = Document.createDocument(record);
            if (document != null) {
                documents.add(document);
            }
        }
    }*/
    public DocumentList(String input) throws IOException {
        BufferedReader reader = new BufferedReader( new InputStreamReader( new FileInputStream( new File(input)),"gbk"));
        String s = null;
        int i = 0;
        while ((s=reader.readLine())!=null) {
            String arry[] =s.split("/t");
            String content = s.substring(arry[0].length()).trim();
            String title =arry[0];
            Document document = new Document(i, content, title);
            documents.add(document);
            i++;
        }
        reader.close();
    }
    /** Add a document to the DocumentList. */
    public void add(Document document) {
        documents.add(document);
    }

    /** Clear all documents from the DocumentList. */
    public void clear() {
        documents.clear();
    }

    /** Mark all documents as not being allocated to a cluster. */
    public void clearIsAllocated() {
        for (Document document : documents) {
            document.clearIsAllocated();
        }
    }

    /** Get a particular document from the DocumentList. */
    public Document get(int index) {
        return documents.get(index);
    }

    /** Get the number of features used to encode each document. */
    public int getNumFeatures() {
        return numFeatures;
    }

    /** Determine whether DocumentList is empty. */
    public boolean isEmpty() {
        return documents.isEmpty();
    }

    @Override
    public Iterator<Document> iterator() {
        return documents.iterator();
    }

    /** Set the number of features used to encode each document. */
    public void setNumFeatures(int numFeatures) {
        this.numFeatures = numFeatures;
    }

    /** Get the number of documents within the DocumentList. */
    public int size() {
        return documents.size();
    }

    /** Sort the documents within the DocumentList by document ID. */
    public void sort() {
        Collections.sort(documents);
    }

    @Override
    public String toString() {
        StringBuilder sb = new StringBuilder();
        for (Document document : documents) {
            sb.append("  ");
            sb.append(document.toString());
            sb.append("/n");
        }
        return sb.toString();
    }
}

其次,针对KMeansClusterer,我们做了如下修改,因为我想要自定义k,而源码作者提供了自动调节k值的方法。

package com.clustering;

import java.util.Random;

/** A Clusterer implementation based on k-means clustering. */
public class KMeansClusterer implements Clusterer {
    private static final Random RANDOM = new Random();
    private final double clusteringThreshold;
    private final int clusteringIterations;
    private final DistanceMetric distance;

    /**
     * Construct a Clusterer.
     * 
     * @param distance the distance metric to use for clustering
     * @param clusteringThreshold the threshold used to determine the number of clusters k
     * @param clusteringIterations the number of iterations to use in k-means clustering
     */
    public KMeansClusterer(DistanceMetric distance, double clusteringThreshold,
        int clusteringIterations) {
        this.distance = distance;
        this.clusteringThreshold = clusteringThreshold;
        this.clusteringIterations = clusteringIterations;
    }

    /**
     * Allocate any unallocated documents in the provided DocumentList to the nearest cluster in the
     * provided ClusterList.
     */
    private void allocatedUnallocatedDocuments(DocumentList documentList, ClusterList clusterList) {
        for (Document document : documentList) {
            if (!document.isAllocated()) {
                Cluster nearestCluster = clusterList.findNearestCluster(distance, document);
                nearestCluster.add(document);
            }
        }
    }

    /**
     * Run k-means clustering on the provided documentList. Number of clusters k is set to the lowest
     * value that ensures the intracluster to intercluster distance ratio is below
     * clusteringThreshold.
     */
    @Override
    public ClusterList cluster(DocumentList documentList) {
        ClusterList clusterList = null;
        for (int k = 1; k <= documentList.size(); k++) {
            clusterList = runKMeansClustering(documentList, k);
            if (clusterList.calcIntraInterDistanceRatio(distance) < clusteringThreshold) {
                break;
            }
        }
        return clusterList;
    }

    /** Create a cluster with the unallocated document that is furthest from the existing clusters. */
    private Cluster createClusterFromFurthestDocument(DocumentList documentList,
        ClusterList clusterList) {
        Document furthestDocument = clusterList.findFurthestDocument(distance, documentList);
        Cluster nextCluster = new Cluster(furthestDocument);
        return nextCluster;
    }

    /** Create a cluster with a single randomly seelcted document from the provided DocumentList. */
    private Cluster createClusterWithRandomlySelectedDocument(DocumentList documentList) {
        int rndDocIndex = RANDOM.nextInt(documentList.size());
        Cluster initialCluster = new Cluster(documentList.get(rndDocIndex));
        return initialCluster;
    }

    /** Run k means clustering on the provided DocumentList for a fixed number of clusters k. */
    public ClusterList runKMeansClustering(DocumentList documentList, int k) {
        ClusterList clusterList = new ClusterList();
        documentList.clearIsAllocated();
        clusterList.add(createClusterWithRandomlySelectedDocument(documentList));
        while (clusterList.size() < k) {
            clusterList.add(createClusterFromFurthestDocument(documentList, clusterList));
        }
        for (int iter = 0; iter < clusteringIterations; iter++) {
            allocatedUnallocatedDocuments(documentList, clusterList);
            clusterList.updateCentroids();
            if (iter < clusteringIterations - 1) {
                clusterList.clear();
            }
        }
        return clusterList;
    }
}
package com.clustering;

/**
 * An interface defining a Clusterer. A Clusterer groups documents into Clusters based on similarity
 * of their content.
 */
public interface Clusterer {
    /** Cluster the provided list of documents. */
    public ClusterList cluster(DocumentList documentList);
    public ClusterList runKMeansClustering(DocumentList documentList, int k);
}

针对接口Clusterer ,其包含两类实现方法,其一是自动确定k数目的方法;其二是用户自定义k值的方法。

结果输出部分

该部分,是自己写的一个类,用于输出聚类结果,以及类单词出现的概率(这里直接计算的是单词在该类中的频率),可自行定义输出topk个单词。具体代码如下:

package com.clustering;

import java.io.BufferedWriter;
import java.io.File;
import java.io.FileOutputStream;
import java.io.IOException;
import java.io.OutputStreamWriter;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Comparator;
import java.util.Hashtable;
import java.util.List;
import java.util.Map;
import java.util.Map.Entry;


public class OutPutFile {
    public static void outputdocument(String strDir,ClusterList clusterList) throws IOException{
        BufferedWriter Writer = new BufferedWriter( new OutputStreamWriter( new FileOutputStream( new File(strDir)),"gbk"));
        for (Cluster cluster : clusterList) {
            //          System.out.println(cluster1.getDocuments());
            String text = "";
            for (Document doc: cluster.getDocuments()) {
                text +=doc.getContents()+" ";
            }
            Writer.write(text+"/n");
        }
        Writer.close();
    }
    public static void outputcluster(String strDir,ClusterList clusterList) throws IOException{
        BufferedWriter Writer = new BufferedWriter( new OutputStreamWriter( new FileOutputStream( new File(strDir)),"gbk"));
        Writer.write(clusterList.toString());
        Writer.close();
    }
    public static void outputclusterwprdpro(String strDir,ClusterList clusterList,int topword) throws IOException{
        BufferedWriter Writer = new BufferedWriter( new OutputStreamWriter( new FileOutputStream( new File(strDir)),"gbk"));
        Hashtable<Integer,String> clusterdocumentlist = new Hashtable<Integer,String>();
        int clusterid=0;
        for (Cluster cluster : clusterList) {
            String text = "";
            for (Document doc: cluster.getDocuments()) {
                text +=doc.getContents()+" ";
            }
            clusterdocumentlist.put(clusterid,text);
            clusterid++;
        }
        for (Integer key : clusterdocumentlist.keySet()) {
            Writer.write("Topic" + new Integer(key) + "/n");
            List<Entry<String, Double>> list=oneclusterwprdpro(clusterdocumentlist.get(key));
            int count=0;
            for (Map.Entry<String, Double> mapping : list) { 
                if (count<=topword) {
                    Writer.write("/t" + mapping.getKey() + " " + mapping.getValue()+ "/n"); 
                    count++;
                }else {
                    break;
                }
            } 
        } 
        Writer.close();
    }
    //词频统计并排序
    public static List<Entry<String, Double>> oneclusterwprdpro(String text){
        Hashtable<String, Integer>  wordCount = new Hashtable<String, Integer>();
        String arry[] =text.split("//s+");
        //词频统计
        for (int i = 0; i < arry.length; i++) {
            if (!wordCount.containsKey(arry[i])) {
                wordCount.put(arry[i], Integer.valueOf(1));
            } else {
                wordCount.put(arry[i], Integer.valueOf(wordCount.get(arry[i]).intValue() + 1));
            }
        }
        //频率计算
        Hashtable<String, Double>  wordpro = new Hashtable<String, Double>();
        for (java.util.Map.Entry<String, Integer> j : wordCount.entrySet()) {
            String key = j.getKey();
            double value = 1.0*j.getValue()/arry.length;
            wordpro.put(key, value);
        }
        //将map.entrySet()转换成list  
        List<Map.Entry<String, Double>> list = new ArrayList<Map.Entry<String, Double>>(wordpro.entrySet());  
        Collections.sort(list, new Comparator<Map.Entry<String, Double>>() {  
            //降序排序  
            public int compare(Entry<String, Double> o1, Entry<String, Double> o2) {  
                //return o1.getValue().compareTo(o2.getValue());  
                return o2.getValue().compareTo(o1.getValue());  
            }  
        });

        return list;
    }
}

主方法

package web.main;

import java.io.IOException;

import com.clustering.ClusterList;
import com.clustering.Clusterer;
import com.clustering.CosineDistance;
import com.clustering.DistanceMetric;
import com.clustering.DocumentList;
import com.clustering.Encoder;
import com.clustering.KMeansClusterer;
import com.clustering.OutPutFile;
import com.clustering.TfIdfEncoder;

/**
 * Solution for Newsle Clustering question from CodeSprint 2012. This class implements clustering of
 * text documents using Cosine or Jaccard distance between the feature vectors of the documents
 * together with k means clustering. The number of clusters is adapted so that the ratio of the
 * intracluster to intercluster distance is below a specified threshold.
 */
public class ClusterDocumentsArgs {
    private static final int CLUSTERING_ITERATIONS = 30;
    private static final double CLUSTERING_THRESHOLD = 0.5;
    private static final int NUM_FEATURES =10000;
    private static final int k = 30;  //自行定义k
    /**
     * Cluster the text documents in the provided file. The clustering process consists of parsing and
     * encoding documents, and then using Clusterer with a specific Distance measure.
     */
    public static void main(String[] args) throws IOException {
        String fileinput = "/home/qianyang/kmeans/webdata/content";
        DocumentList documentList = new DocumentList(fileinput);
        Encoder encoder = new TfIdfEncoder(NUM_FEATURES);
        encoder.encode(documentList);
        System.out.println(documentList.size());
        DistanceMetric distance = new CosineDistance();
        Clusterer clusterer = new KMeansClusterer(distance, CLUSTERING_THRESHOLD, CLUSTERING_ITERATIONS);
        ClusterList clusterList = clusterer.runKMeansClustering(documentList, k);
//      ClusterList clusterList = clusterer.cluster(documentList);
        //输出聚类结果
        OutPutFile.outputcluster("/home/qianyang/kmeans/result/cluster"+k,clusterList);
        //输出topk个单词
        OutPutFile.outputclusterwprdpro("/home/qianyang/kmeans/result/wordpro"+k+"and10", clusterList, 10);
        OutPutFile.outputclusterwprdpro("/home/qianyang/kmeans/result/wordpro"+k+"and15", clusterList, 15);
        OutPutFile.outputclusterwprdpro("/home/qianyang/kmeans/result/wordpro"+k+"and20", clusterList, 20);
        OutPutFile.outputclusterwprdpro("/home/qianyang/kmeans/result/wordpro"+k+"and25", clusterList, 25);
    }
}

如下图所示为结果,我们可以看出每个簇下面的所聚集的文档有哪些。


基于Kmeans算法的文档聚类(包含Java代码及数据格式)

如下图所示为簇下单词的频率。

基于Kmeans算法的文档聚类(包含Java代码及数据格式)


如果感觉基于频率计算得到的topk个单词区分度不明显,可再次使用tf-idf进行处理,这里就不做过多的介绍了。

原文 

http://blog.csdn.net/qy20115549/article/details/80530117

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