Java学习（Day 28）

学习来源：日撸 Java 三百行（51-60天，kNN 与 NB）_闵帆的博客-CSDN博客

文章目录

kMeans 聚类
- 一、聚类
- 二、kMeans步骤
- 三、代码分析
- - 1. 流程
  - 2. 完整代码
  - 3. 运行截图
总结

kMeans 聚类一、聚类

监督式学习: 训练集有明确答案, 监督学习就是寻找问题 (又称输入、特征、自变量) 与答案 (又称输出、目标、因变量) 之间关系的学习方式. 监督学习模型有两类, 分类和回归.

分类模型: 目标变量是离散的分类型变量
回归模型: 目标变量是连续性数值型变量

无监督式学习: 只有数据, 无明确答案, 即训练集没有标签. 常见的无监督学习算法有聚类 (clustering), 由计算机自己找出规律, 把有相似属性的样本放在一组, 每个组也称为簇 (cluster). 接下来要谈到的 KMeans 就是其中之一.

二、kMeans步骤

K-Means 聚类步骤是一个循环迭代的算法, 非常简单易懂：

假定我们要对 N 个样本观测做聚类, 要求聚为 K 类, 首先选择 K 个点作为初始中心点
接下来, 按照距离初始中心点最小的原则, 把所有观测分到各中心点所在的类中
每类中有若干个观测, 计算 K 个类中所有样本点的均值, 作为第二次迭代的 K 个中心点
然后根据这个中心重复第2、3步，直到收敛 (中心点不再改变或达到指定的迭代次数), 聚类过程结束

三、代码分析 1. 流程

Step 1: 从特定文件 iris.arff 读入数据存放在一个特定的数据结构中, 我将它命名为 dataset.

Step 2: 人为地为最后要分的类个数做出规定, 我们这里把类别固定为3, 当然这一操作就感觉是一种上帝视角了.

Step 3: 开始聚类

Step 3.1: 获得一个混淆后的整数数组, 其中存放的是下标, 这个下标指的是数据在 dataset 里的下标.

Step 3.2: 用欧式距离找到各点到中心点距离, 然后把它归到最近的那个类中.

Step 3.3: 重新找中心点, 就是将每个类中所有数据各类别加起来取平均值. 重复 Step 3的步骤直到中心点不变.

2. 完整代码

package kmeans;

import java.io.FileReader;
import java.util.Arrays;
import java.util.Random;

import weka.core.Instances;


public class KMeans {
    
    public static final int MANHATTAN = 0;

    
    public static final int EUCLIDEAN = 1;

    
    public int distanceMeasure = EUCLIDEAN;

    
    public static final Random random = new Random();

    
    Instances dataset;

    
    int numClusters = 2;

    
    int[][] clusters;

    
    public KMeans(String paraFilename) {
        dataset = null;
        try {
            FileReader fileReader = new FileReader(paraFilename);
            dataset = new Instances(fileReader);
            fileReader.close();
        } catch (Exception ee) {
            System.out.println("Cannot read the file: " + paraFilename + "rn" + ee);
            System.exit(0);
        } // Of try
    }// Of the first constructor

    
    public void setNumClusters(int paraNumClusters) {
        numClusters = paraNumClusters;
    }// Of the setter

    
    public static int[] getRandomIndices(int paraLength) {
        int[] resultIndices = new int[paraLength];

        // Step 1. Initialize.
        for (int i = 0; i < paraLength; i++) {
            resultIndices[i] = i;
        } // Of for i

        // Step 2. Randomly swap.
        int tempFirst, tempSecond, tempValue;
        for (int i = 0; i < paraLength; i++) {
            // Generate two random indices.
            tempFirst = random.nextInt(paraLength);
            tempSecond = random.nextInt(paraLength);

            // Swap.
            tempValue = resultIndices[tempFirst];
            resultIndices[tempFirst] = resultIndices[tempSecond];
            resultIndices[tempSecond] = tempValue;
        } // Of for i

        return resultIndices;
    }// Of getRandomIndices

    
    public double distance(int paraI, double[] paraArray) {
        int resultDistance = 0;
        double tempDifference;
        switch (distanceMeasure) {
            case MANHATTAN:
                for (int i = 0; i < dataset.numAttributes() - 1; i++) {
                    tempDifference = dataset.instance(paraI).value(i) - paraArray[i];
                    if (tempDifference < 0) {
                        resultDistance -= tempDifference;
                    } else {
                        resultDistance += tempDifference;
                    } // Of if
                } // Of for i
                break;

            case EUCLIDEAN:
                for (int i = 0; i < dataset.numAttributes() - 1; i++) {
                    tempDifference = dataset.instance(paraI).value(i) - paraArray[i];
                    resultDistance += tempDifference * tempDifference;
                } // Of for i
                break;
            default:
                System.out.println("Unsupported distance measure: " + distanceMeasure);
        }// Of switch

        return resultDistance;
    }// Of distance

    
    public void clustering() {
        int[] tempOldClusterArray = new int[dataset.numInstances()];
        tempOldClusterArray[0] = -1;
        int[] tempClusterArray = new int[dataset.numInstances()];
        Arrays.fill(tempClusterArray, 0);
        double[][] tempCenters = new double[numClusters][dataset.numAttributes() - 1];

        // Step 1. Initialize centers.
        int[] tempRandomOrders = getRandomIndices(dataset.numInstances());
        for (int i = 0; i < numClusters; i++) {
            for (int j = 0; j < tempCenters[0].length; j++) {
                tempCenters[i][j] = dataset.instance(tempRandomOrders[i]).value(j);
            } // Of for j
        } // Of for i

        int[] tempClusterLengths = null;
        while (!Arrays.equals(tempOldClusterArray, tempClusterArray)) {
            System.out.println("New loop ...");
            tempOldClusterArray = tempClusterArray;
            tempClusterArray = new int[dataset.numInstances()];

            // Step 2.1 Minimization. Assign cluster to each instance.
            int tempNearestCenter;
            double tempNearestDistance;
            double tempDistance;

            for (int i = 0; i < dataset.numInstances(); i++) {
                tempNearestCenter = -1;
                tempNearestDistance = Double.MAX_VALUE;

                for (int j = 0; j < numClusters; j++) {
                    tempDistance = distance(i, tempCenters[j]);
                    if (tempNearestDistance > tempDistance) {
                        tempNearestDistance = tempDistance;
                        tempNearestCenter = j;
                    } // Of if
                } // Of for j
                tempClusterArray[i] = tempNearestCenter;
            } // Of for i

            // Step 2.2 Mean. Find new centers.
            tempClusterLengths = new int[numClusters];
            Arrays.fill(tempClusterLengths, 0);
            double[][] tempNewCenters = new double[numClusters][dataset.numAttributes() - 1];
            // Arrays.fill(tempNewCenters, 0);
            for (int i = 0; i < dataset.numInstances(); i++) {
                for (int j = 0; j < tempNewCenters[0].length; j++) {
                    tempNewCenters[tempClusterArray[i]][j] += dataset.instance(i).value(j);
                } // Of for j
                tempClusterLengths[tempClusterArray[i]]++;
            } // Of for i

            // Step 2.3 Now average
            for (int i = 0; i < tempNewCenters.length; i++) {
                for (int j = 0; j < tempNewCenters[0].length; j++) {
                    tempNewCenters[i][j] /= tempClusterLengths[i];
                } // Of for j
            } // Of for i

            System.out.println("Now the new centers are: " + Arrays.deepToString(tempNewCenters));
            tempCenters = tempNewCenters;
        } // Of while

        // Step 3. Form clusters.
        clusters = new int[numClusters][];
        int[] tempCounters = new int[numClusters];
        for (int i = 0; i < numClusters; i++) {
            if (tempClusterLengths != null) {
                clusters[i] = new int[tempClusterLengths[i]];
            }
        } // Of for i

        for (int i = 0; i < tempClusterArray.length; i++) {
            clusters[tempClusterArray[i]][tempCounters[tempClusterArray[i]]] = i;
            tempCounters[tempClusterArray[i]]++;
        } // Of for i

        System.out.println("The clusters are: rn");

        for (int i = 0; i < clusters.length; i++) {
            System.out.print("clusters " + i + ": ");
            for (int j = 0; j < clusters[i].length; j++) {
                System.out.print( clusters[i][j] + " ");
            }
            System.out.println();
        }
    }// Of clustering

    
    public static void testClustering() {
        KMeans tempKMeans = new KMeans("D:/Work/sampledata/iris.arff");
        tempKMeans.setNumClusters(3);
        tempKMeans.clustering();
    }// Of testClustering

    
    public static void main(String[] args) {
        testClustering();
    }// Of main
} // Of class KMeans

3. 运行截图

总结

kMeans 优点在于原理简单, 容易实现, 聚类效果好.

当然, 也有一些缺点：

K 值、初始点的选取不好确定.
得到的结果只是局部最优.
受离群值影响大.

Java学习（Day 28）

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