红酒数据集聚类

红酒数据集聚类"/>

红酒数据集聚类

红酒数据聚类

​ Wine 数据集包含来自 3 种不同起源的葡萄酒的共 178 条记录（共 178 种葡萄酒），13 个属 性是葡萄酒的 13 种化学成分，通过化学分析可以推断葡萄酒的起源。
​ 要求实现一种聚类方法，完成 178 条记录的聚类，把整个数据集中的数据分成 3 类（对应 3 种不同的起源的葡萄酒）

程序代码

'''
-*- coding:utf-8 -*-
时间：2019-6-6 星期四
作者：zzx
功能：红酒数据集聚类
'''
import random
import numpy as np
from sklearn import preprocessing
def selectInitMeanVec(Data,k):#选择初始均值向量indexInitMeanVec = random.sample(range(m), k)initMeanVec = Data[indexInitMeanVec, :]return initMeanVecdef calcDistance(Data, k, MeanVec):#计算距离并归入簇中Dist = np.zeros((k, 1))Label = np.zeros((m, 1))for i in range(m):for j in range(k):a = Data[i, :]-MeanVec[j, :]Dist[j] = np.sqrt(sum(a**2))Label[i] = np.argmin(Dist)return Labeldef updateMeanVec(Data, Label, k, oldMeanVec):#更新均值向量newMeanVec = np.zeros((k, n))numSamples = np.zeros((k, 1), dtype=int)for i in range(k):num = 0D = np.zeros((k, 0))for j in range(m):if Label[j] == i:D = np.append(D, Data[j, :])num += 1numSamples[i] = numD = np.reshape(D, (-1, n))newMeanVec[i, :] = np.mean(D, axis=0)#如果本次更新后某一簇中无样本，取上一次均值向量为本次均值向量if num == 0:newMeanVec[i, :] = oldMeanVec[i, :]return newMeanVec, numSamplesif __name__ == '__main__':data = np.loadtxt("wine.csv", delimiter=',')[:, 0:14]Data = preprocessing.scale(data)k = 3global m, nm, n = Data.shapeinitMeanVec = selectInitMeanVec(Data, k)oldMeanVec = initMeanVec.copy()Label = calcDistance(Data, k, initMeanVec)for i in range(200):newMeanVec, numSamples = updateMeanVec(Data, Label, k, oldMeanVec)oldMeanVec = newMeanVec.copy()Label = calcDistance(Data, k, newMeanVec)# print(Label, Data)print('---第%d轮迭代完成'%(i+1))print(numSamples)l = 1for L in Label:if 0 in Label[l]:print("第一个簇:%d" %l)elif 1 in Label[l]:print("第二个簇:%d" %l)elif 2 in Label[l]:print("第三个簇:%d" %l)else:passl += 1