import numpy as np from sklearn.cluster import KMeans def loadData(filePath): fr=open(filePath,'r+') lines=fr.readlines() rownum=len(lines) retData=[] retCityName=[] #skipe the first row for line in range(1,rownum): items=lines[line].strip().split("\t") retCityName.append(items[0]) retData1=[] for i in range(1,len(items)): retData1.append(float(items[i])) retData.append(retData1) fr.close() return retData,retCityName if __name__=='__main__': data,cityName=loadData('d:/test/city.txt') km=KMeans(n_clusters=3) label=km.fit_predict(data) expenses=np.sum(km.cluster_centers_,axis=1) #print(expenses) CityCluster=[[],[],[]] for i in range(len(cityName)): CityCluster[label[i]].append(cityName[i]) for i in range(len(CityCluster)): print("Expenses:%.2f"% expenses[i]) print(CityCluster[i])
python-kmeans
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