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\"\"<\/p>\n

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\n

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\u5728\u4ea7\u54c1\u5f00\u53d1\u6a21\u5f0f\u65b9\u9762，\u672c\u6587\u5efa\u8baeA\u516c\u53f8\u91c7\u7528\u654f\u6377\u5f00\u53d1\u6a21\u5f0f，\u7ed3\u5408kano-QFD\u8026\u5408\u5206\u6790\u7ed3\u679c，\u5c06\u5ba2\u6237\u9700\u6c42\u7eb3\u5165\u5230\u6bcf\u4e00\u4e2a\u5f00\u53d1\u8fed\u4ee3\u4e2d\u3002\u654f\u6377\u5f00\u53d1\u4f7f\u5f97\u5f00\u53d1\u56e2\u961f\u80fd\u591f\u5feb\u901f\u54cd\u5e94\u9700\u6c42\u53d8\u5316，\u5e76\u901a\u8fc7\u591a\u6b21\u8fed\u4ee3\u9010\u6b65\u5b8c\u5584\u4ea7\u54c1\u529f\u80fd，\u4fdd\u8bc1\u6700\u7ec8\u4ea7\u54c1\u80fd\u591f\u7d27\u8ddf\u5e02\u573a\u9700\u6c42\u3002\u540c\u65f6，\u654f\u6377\u5f00\u53d1\u6a21\u5f0f\u4e0b\u7684\u6301\u7eed\u5ba2\u6237\u53cd\u9988\u673a\u5236，\u80fd\u591f\u5728\u4ea7\u54c1\u5f00\u53d1\u7684\u6bcf\u4e2a\u9636\u6bb5\u9a8c\u8bc1\u8bbe\u8ba1\u65b9\u6848\u7684\u53ef\u884c\u6027，\u5e76\u53ca\u65f6\u8fdb\u884c\u8c03\u6574\u3002<\/p>\n

\u4e3a\u4e86\u652f\u6301\u4e0a\u8ff0\u5f00\u53d1\u6a21\u5f0f\u7684\u987a\u5229\u5b9e\u65bd，A\u516c\u53f8\u9700\u8981\u5728\u7ec4\u7ec7\u5185\u90e8\u5efa\u7acb\u9ad8\u6548\u7684\u8de8\u90e8\u95e8\u534f\u4f5c\u673a\u5236，\u786e\u4fdd\u7814\u53d1\u3001\u5e02\u573a\u548c\u552e\u540e\u56e2\u961f\u4e4b\u95f4\u7684\u4fe1\u606f\u5171\u4eab\u548c\u65e0\u7f1d\u5bf9\u63a5\u3002\u901a\u8fc7\u5efa\u7acb\u8de8\u90e8\u95e8\u534f\u4f5c\u673a\u5236，\u5e02\u573a\u56e2\u961f\u80fd\u591f\u53ca\u65f6\u5c06\u5ba2\u6237\u7684\u53cd\u9988\u4f20\u9012\u7ed9\u7814\u53d1\u56e2\u961f，\u786e\u4fdd\u4ea7\u54c1\u8bbe\u8ba1\u548c\u5f00\u53d1\u80fd\u591f\u5145\u5206\u53cd\u6620\u5ba2\u6237\u9700\u6c42\u3002\u540c\u65f6，\u7814\u53d1\u56e2\u961f\u4e5f\u80fd\u591f\u5bf9\u5e02\u573a\u9700\u6c42\u63d0\u4f9b\u6280\u672f\u53ef\u884c\u6027\u5206\u6790，\u4ece\u800c\u66f4\u597d\u5730\u5e73\u8861\u5ba2\u6237\u9700\u6c42\u4e0e\u6280\u672f\u5b9e\u73b0\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/p>\n

\u6700\u540e，\u672c\u6587\u8fd8\u5efa\u8baeA\u516c\u53f8\u901a\u8fc7\u6570\u636e\u5206\u6790\u548c\u4eba\u5de5\u667a\u80fd\u624b\u6bb5\u63d0\u5347\u9700\u6c42\u5206\u6790\u7684\u7cbe\u51c6\u5ea6\u3002\u4f8b\u5982，\u53ef\u4ee5\u501f\u52a9\u673a\u5668\u5b66\u4e60\u5bf9\u5ba2\u6237\u5386\u53f2\u8ba2\u5355\u6570\u636e\u548c\u5e02\u573a\u8d8b\u52bf\u8fdb\u884c\u5206\u6790，\u9884\u6d4b\u672a\u6765\u7684\u5ba2\u6237\u9700\u6c42\u53d8\u5316，\u5e76\u636e\u6b64\u8c03\u6574\u4ea7\u54c1\u5f00\u53d1\u7b56\u7565\u3002\u901a\u8fc7\u4e0e\u5927\u6570\u636e\u6280\u672f\u7ed3\u5408，A\u516c\u53f8\u53ef\u4ee5\u66f4\u7cbe\u786e\u5730\u5b9a\u4f4d\u5e02\u573a\u4e2d\u7684\u6f5c\u5728\u9700\u6c42，\u4ece\u800c\u63d0\u524d\u5e03\u5c40\u65b0\u6280\u672f<\/p>\n

import numpy as np
\nimport pandas as pd
\nfrom sklearn.cluster import KMeans
\nimport matplotlib.pyplot as plt<\/p>\n

# \u6a21\u62df\u5ba2\u6237\u9700\u6c42\u6570\u636e
\nnp.random.seed(42)
\ndata = pd.DataFrame({
\n 'stability': np.random.uniform(1, 5, 100),
\n 'performance': np.random.uniform(1, 5, 100),
\n 'cost_efficiency': np.random.uniform(1, 5, 100),
\n 'usability': np.random.uniform(1, 5, 100),
\n 'expandability': np.random.uniform(1, 5, 100)
\n})<\/p>\n

# Kano\u6a21\u578b\u5206\u7c7b（\u7b80\u5316）
\ndef kano_classification(row):
\n if row['stability'] > 4 and row['performance'] > 4:
\n return 'Attractive'
\n elif row['cost_efficiency'] > 4:
\n return 'Must-be'
\n elif row['usability'] > 4 or row['expandability'] > 4:
\n return 'One-dimensional'
\n else:
\n return 'Indifferent'<\/p>\n

data['kano_category'] = data.apply(kano_classification, axis=1)<\/p>\n

# \u805a\u7c7b\u5206\u6790
\nkmeans = KMeans(n_clusters=3)
\ndata['cluster'] = kmeans.fit_predict(data[['stability', 'performance', 'cost_efficiency', 'usability', 'expandability']])<\/p>\n

# \u8ba1\u7b97\u6280\u672f\u91cd\u8981\u5ea6（\u7b80\u5316\u7684QFD\u65b9\u6cd5）
\nweights = {'Attractive': 3, 'Must-be': 5, 'One-dimensional': 4, 'Indifferent': 1}
\ndata['importance'] = data['kano_category'].map(weights)<\/p>\n

# \u7ed8\u5236\u805a\u7c7b\u7ed3\u679c
\nplt.figure(figsize=(10, 6))
\nfor cluster in data['cluster'].unique():
\n clustered_data = data[data['cluster'] == cluster]
\n plt.scatter(clustered_data['performance'], clustered_data['expandability'], label=f'Cluster {cluster}')<\/p>\n

plt.xlabel('Performance')
\nplt.ylabel('Expandability')
\nplt.title('Customer Clustering based on Kano-QFD Analysis')
\nplt.legend()
\nplt.show() <\/p>\n

\"\"<\/p><\/p>\n","protected":false},"excerpt":{"rendered":"

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