簡單用k-mean處理iris 數據集

import pandas as pd from sklearn.cluster import KMeans from sklearn.metrics import homogeneity_score, completeness_score, v_measure_scoredata = pd.read_csv('iris.data', header=None, names=['花萼長度', '花萼寬度', '花瓣長度', '花瓣寬度', '類別']) x = data[['花萼長度', '花萼寬度', '花瓣長度', '花瓣寬度']] model = KMeans(n_clusters=3, init='k-means++') model.fit(x) y_pred = model.predict(x) print('homogeneity_score = ', homogeneity_score(data['類別'], y_pred)) print('completeness_score = ', completeness_score(data['類別'], y_pred)) print('v_measure_score = ', v_measure_score(data['類別'], y_pred)) data['Predict'] = y_pred print(data) data.to_csv('result.csv', sep=',', encoding='gbk', index=False) print('Data Save OK....') OUT： homogeneity_sc

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