package?Kmeans;

import?java.io.File;
import?java.io.FileNotFoundException;
import?java.io.FileOutputStream;
import?java.io.PrintStream;
import?java.util.ArrayList;
import?java.util.Random;
import?java.util.Scanner;

public?class?Kmeans?{
	public?static?int?dimension=690;
	public?static?int?count_of_data=320;
	public?static?int?cluster=20;
	public?Kmeans（）?throws?FileNotFoundException{
		ArrayList?dataset=new?ArrayList<>（）;
?????	String?path=“E:\\數據挖掘\\實驗三\\聚類實驗數據\\data.txt“;
//		String?path=“C:\\data.txt“;
		File?file=new?File（path）;
		Scanner?sc=null;
		sc=new?Scanner（file）;
		while（sc.hasNextLine（））{
			Data?data=new?Data（）;
			String?str=sc.nextLine（）;
			String[]?arr=str.split（“?“）;
			for（String?s:arr）{
				data.point.add（Double.parseDouble（s））;
			}
			dataset.add（data）;
		}
		File?fi?=?new?File（“K-mean“?+?cluster?+?“.txt“）;
		FileOutputStream?fs?=?new?FileOutputStream（fi）;
		PrintStream?ps?=?new?PrintStream（fs）;
//		隨機取cluster個點
		ArrayList?initial=new?ArrayList<>（）;
		Random?rand?=new?Random（）;
		for（int?i=0;i			int?x;
			while（initial.contains（x=rand.nextInt（count_of_data））==true）{}
			initial.add（x）;
		}
//		生成cluster個簇
		ArrayList?crowdset=new?ArrayList<>（）;
		for（int?i=0;i			Crowdtogether?crowd=new?Crowdtogether（）;
			crowd.dim=i;
			for（int?j=0;j				crowd.point.add（dataset.get（initial.get（i））.point.get（j））;
			}
			crowdset.add（crowd）;
		}
//		每個點分配簇
		for（int?i=0;i			dyeing（dataset.get（i）idatasetcrowdset）;
		}
//		while循環
		boolean?flag=false;
		while（flag==false）{
//			取平均值
			for（int?i=0;i				double?sumDistance=0;
				double?count=0;
				Double[]?pt=new?Double[dimension];
				for（int?x=0;x					pt[x]=0.0;
				}
				for（int?j=0;j					if（dataset.get（j）.dim==i）{
						for（int?k=0;k							pt[k]=pt[k]+dataset.get（j）.point.get（k）;
						}
						count++;
					}
				}
				for（int?k=0;k