{"id":15020,"date":"2022-02-12T16:38:00","date_gmt":"2022-02-12T08:38:00","guid":{"rendered":"https:\/\/achieve.dhcn.cn\/?p=15020"},"modified":"2024-04-04T01:29:04","modified_gmt":"2024-04-03T17:29:04","slug":"%e3%80%8a%e6%b1%89%e4%b9%a6%c2%b7%e8%89%ba%e6%96%87%e5%bf%97%e3%80%8b%e7%9b%ae%e5%bd%95%e5%88%86%e7%b1%bb%e5%86%8d%e5%ae%a1%e8%a7%86","status":"publish","type":"post","link":"https:\/\/achieve.dhcn.cn\/en\/site\/works\/dhjournal\/202103\/15020.html","title":{"rendered":"\u300a\u6c49\u4e66\u00b7\u827a\u6587\u5fd7\u300b\u76ee\u5f55\u5206\u7c7b\u518d\u5ba1\u89c6"},"content":{"rendered":"

\u4f5c\u8005\uff1a \u8bf8\u96e8\u8fb0 \u674e\u7ec5 <\/strong>\uff1b\u8f6c\u81ea\uff1a\u516c\u4f17\u53f7 DH\u6570\u5b57\u4eba\u6587 <\/p>\n\n\n\n

Infrastructure<\/strong><\/span><\/strong><\/p>\n\n\n\n

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\u8bf8\u96e8\u8fb0<\/strong> <\/strong>\/ <\/strong>\u5317\u4eac\u5e08\u8303\u5927\u5b66\u6587\u5b66\u9662<\/strong><\/p>\n\n\n\n

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——————————<\/p>\n\n\n\n

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———————————— <\/p>\n\n\n\n

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\u4e00\u3001\u300a\u6c49\u5fd7\u300b\u6587\u672c\u5206\u7c7b\u7684\u53ef\u80fd\u6027<\/strong><\/p>\n\n\n\n

\u8ba1\u7b97\u8bed\u8a00\u5b66\u901a\u8fc7\u8ba1\u7b97\u6587\u672c\u7684\u76f8\u4f3c\u6027\u5b9e\u73b0\u5bf9\u6587\u672c\u8bed\u4e49\u4fe1\u606f\u7684\u7406\u89e3\u4e0e\u52a0\u5de5\u3002\u57fa\u4e8eHarris\u7684\u8bcd\u4e49\u5206\u5e03\u5047\u8bf4[7]<\/sup>\u2014\u2014\u4e0a\u4e0b\u6587\u76f8\u4f3c\u7684\u8bcd\uff0c\u5176\u610f\u4e49\u4e5f\u76f8\u8fd1\uff0c\u4eba\u4eec\u5efa\u7acb\u4e86\u8bcd\u5411\u91cf\u6a21\u578b\u3002\u5728\u8fdb\u4e00\u6b65\u6539\u8fdb\u7b97\u6cd5\u57fa\u7840\u4e0a\uff0cMikolov\u7b49\u63d0\u51fa\u4e86\u795e\u7ecf\u8bcd\u5411\u91cf(Neural Word Embeddings)\u6a21\u578b\uff0c[8]<\/sup>\u5728\u5927\u89c4\u6a21\u8bed\u6599\u5e93\u4e2d\uff0c\u4f9d\u6b21\u9009\u53d6\u4e2d\u5fc3\u8bcd\u53ca\u5176\u4e0a\u4e0b\u6587\u7684\u4e34\u8fd1\u8bcd\uff0c\u901a\u8fc7\u795e\u7ecf\u7f51\u7edc\u6784\u5efa\u4e2d\u5fc3\u8bcd\u4e0e\u4e0a\u4e0b\u6587\u5173\u7cfb\u7684\u76f8\u4e92\u9884\u6d4b\u6a21\u578b\uff0c\u4ece\u800c\u66f4\u6709\u6548\u5730\u63d0\u5347\u6587\u672c\u7279\u5f81\u8868\u793a\u7684\u6548\u679c\u3002\u800c\u901a\u8fc7\u8bcd\u5411\u91cf\u6a21\u578b\u7684\u8bed\u4e49\u8868\u5f81\u53ca\u8bed\u4e49\u8ba1\u7b97\uff0c\u4e5f\u5c31\u5b9e\u73b0\u4e86\u8bf8\u5982\u6587\u672c\u5206\u7c7b\u3001\u6587\u672c\u805a\u7c7b\u3001\u56de\u5f52\u9884\u6d4b\u7b49\u8bed\u4e49\u7406\u89e3\u4efb\u52a1\u3002<\/p>\n\n\n\n

\u8003\u8651\u5230\u53e4\u6c49\u8bed\u548c\u73b0\u4ee3\u6c49\u8bed\u7684\u8bed\u4e49\u548c\u8bed\u6cd5\u6709\u663e\u8457\u7684\u533a\u522b\uff0c\u672c\u7814\u7a76\u4f7f\u7528\u4e86Shen Li\u7b49\u4eba\u57fa\u4e8e\u300a\u56db\u5e93\u5168\u4e66\u300b\u8bad\u7ec3\u7684\u53e4\u6c49\u8bed\u795e\u7ecf\u8bcd\u5411\u91cf\u6a21\u578b[9]<\/sup>\u3002\u9996\u5148\u5bf9\u6bcf\u7bc7\u6587\u672c\u4e2d\u7684\u5b57\u67e5\u627e\u5176\u5728\u5411\u91cf\u6a21\u578b\u4e2d\u7684300\u7ef4\u5411\u91cf\u8868\u793a\uff0c\u7136\u540e\u5bf9\u6bcf\u7bc7\u6587\u672c\u5404\u4e2a\u5b57\u5411\u91cf\u53d6\u5e73\u5747\u5f97\u5230\u6587\u672c\u7684300\u7ef4\u5411\u91cf\u8868\u793a\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u7ed9\u5b9a\u957f\u5ea6\u4e3an\u7684\u6587\u672cd=(w1,w2,…,wi,…,wn-1,wn)\uff0c\u5176\u4e2dwi\u662f\u7ec4\u6210\u6587\u672c\u7684\u5b57\u3002\u5728\u9884\u8bad\u7ec3\u7684\u5b57\u5411\u91cf\u4e2d\u67e5\u627e\u5230\u6bcf\u4e2awi\u5bf9\u5e94\u7684\u5411\u91cfei(\u7ef4\u5ea6\u662f300)\uff0c\u5219\u6587\u672cd\u7684\u5411\u91cf\u8868\u793a\u3002\u4e4b\u540e\u4f7f\u7528\u65e0\u76d1\u7763\u7684\u81ea\u52a8\u805a\u7c7b\u6a21\u578b\uff0c\u5bf9\u76f8\u5173\u6587\u672c\u8fdb\u884c\u805a\u7c7b\u3002\u805a\u7c7b\u6a21\u578b\u7684\u57fa\u672c\u601d\u8def\u662f\u521d\u59cb\u5316K\u4e2a\u4e2d\u5fc3\u6837\u672c\uff0c\u5176\u4f59\u6587\u672c\u56f4\u7ed5\u4e0e\u5176\u6700\u8fd1\u7684\u4e2d\u5fc3\u6837\u672c\u5f62\u6210K\u4e2a\u6587\u672c\u7c07\uff0c\u7ecf\u8fc7\u53cd\u590d\u8fed\u4ee3\u8c03\u6574\u4e2d\u5fc3\u6837\u672c\u7684\u4f4d\u7f6e\uff0c\u6700\u7ec8\u5f62\u6210\u76f8\u5bf9\u7a33\u5b9a\u7684\u6587\u672c\u7c07\uff0c\u5b8c\u6210\u5bf9\u6587\u672c\u7684\u81ea\u52a8\u805a\u7c7b\u3002\u7531\u4e8e\u4e0d\u540c\u805a\u7c7b\u6a21\u578b\u5728\u521d\u59cb\u5316\u65b9\u5f0f\u4e0e\u539f\u7406\u4e0a\u4e0d\u5c3d\u76f8\u540c\uff0c\u4e00\u822c\u805a\u7c7b\u7684\u7ed3\u679c\u4e5f\u4e0d\u4e00\u6837\uff0c\u672c\u7814\u7a76\u91c7\u7528K-means++[10]<\/sup>\u3001Gaussian Mixture Model[11]<\/sup>\u548cSpectral Clustering[12]<\/sup>\u4e09\u79cd\u805a\u7c7b\u6a21\u578b\uff0c\u5bf9\u6587\u672c\u81ea\u52a8\u805a\u7c7b\u7ed3\u679c\u8fdb\u884c\u7efc\u5408\u5224\u65ad\uff0c\u4f9d\u6b21\u53d6K=2\u81f3K=8\uff0c\u5c06\u4e0a\u8ff078\u79cd\u6587\u732e\u81ea\u52a8\u805a\u4e3a2\u81f38\u7c7b\uff0c\u89c2\u5bdf\u57fa\u4e8e\u6587\u672c\u76f8\u4f3c\u6027\u800c\u5f62\u6210\u7684\u6587\u672c\u5206\u7c7b\u7ed3\u679c\u3002\u5176\u4e2d\uff0c\u4e3a\u4e86\u964d\u4f4e\u805a\u7c7b\u7684\u968f\u673a\u6027\uff0c\u672c\u6587\u9009\u7528\u7684\u662fK-means[13]<\/sup>\u7684\u6539\u8fdb\u7b97\u6cd5K-means++\uff0c\u8f83K-means\u800c\u8a00\uff0c\u5176\u53ef\u4ee5\u6839\u636e\u6570\u636e\u8ba1\u7b97\u51fa\u7a33\u5b9a\u7684\u521d\u59cb\u805a\u7c7b\u4e2d\u5fc3\u70b9\uff0c\u540c\u65f6\u4e3a\u4e86\u5145\u5206\u6536\u655b\uff0c\u6700\u5927\u8fed\u4ee3\u6b21\u6570\u8bbe\u7f6e\u4e3a500\u3002Gaussian Mixture Model\u8bbe\u7f6e\u4e3a\u6bcf\u4e00\u4e2a\u7c7b\u4f7f\u7528\u72ec\u7acb\u7684\u534f\u65b9\u5dee\u77e9\u9635\uff0c\u5176\u4e2d\u7684EM\u8fed\u4ee3\u9608\u503c\u8bbe\u7f6e\u4e3a1e-3\uff0c\u6700\u5927\u8fed\u4ee3100\u6b21\u3002Spectral Clustering\u91c7\u7528\u4e86\u548c\u805a\u7c7b\u6570\u91cf\u76f8\u7b49\u7684\u672c\u5f81\u503c\u5411\u91cf\u4e2a\u6570\uff0c\u5e76\u7528RBF\u6838\u6784\u5efa\u76f8\u4f3c\u5ea6\u77e9\u9635(affinity matrix)\u3002\u6b64\u5916\uff0c\u4e3a\u4e86\u907f\u514d\u5355\u6b21\u5b9e\u9a8c\u5e26\u6765\u7684\u5076\u7136\u6027\uff0c\u6240\u6709\u805a\u7c7b\u5b9e\u9a8c\u5747\u8fdb\u884c\u4e8620\u6b21\uff0c\u9009\u62e9\u5176\u4e2d\u6536\u655b\u7a0b\u5ea6\u6700\u9ad8\u7684\u4e00\u7ec4\u4f5c\u4e3a\u6700\u7ec8\u7ed3\u679c\u3002<\/p>\n\n\n\n

\u88681 K-means++\u4e8c\u5206\u6cd5\u7684\u81ea\u52a8\u805a\u7c7b\u7ed3\u679c<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

\u88682 Gaussian Mixture Model\u4e8c\u5206\u6cd5\u7684\u81ea\u52a8\u805a\u7c7b\u7ed3\u679c<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

\u88683 Spectral Clustering\u4e8c\u5206\u6cd5\u7684\u81ea\u52a8\u805a\u7c7b\u7ed3\u679c <\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

\u4ec5\u5206\u4e24\u7c7b\u65f6(\u88681\u3001\u88682\u3001\u88683)\uff0c\u4e09\u79cd\u6a21\u578b\u7684\u805a\u7c7b\u7ed3\u679c\u90fd\u663e\u73b0\u51fa\u5b66\u672f\u6027\u4e0e\u6587\u5b66\u6027\u7684\u5dee\u5f02\uff0c\u8f9e\u8d4b\u7c7b\u6587\u732e\u5f80\u5f80\u5355\u72ec\u805a\u4e3a\u4e00\u7c7b\u3002\u5176\u4e2dK-means++\u6a21\u578b\u7684\u7ed3\u679c\u76f8\u5bf9\u66f4\u597d\uff0c\u800cGaussian Mixture Model\u6df7\u5165\u4e86\u975e\u8f9e\u8d4b\u7c7b\u7684\u300a\u5c14\u96c5\u300b\u300a\u5c71\u6d77\u7ecf\u300b\uff0cSpectral Clustering \u5219\u628a\u90e8\u5206\u8f9e\u8d4b\u6df7\u5165\u4e86\u5b66\u672f\u7c7b\u3002<\/p>\n\n\n\n

\u88684 K-means++\u4e09\u5206\u6cd5\u7684\u81ea\u52a8\u805a\u7c7b\u7ed3\u679c<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

\u88685 Gaussian Mixture Model\u4e09\u5206\u6cd5\u7684\u81ea\u52a8\u805a\u7c7b\u7ed3\u679c<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

\u88686 Spectral Clustering\u4e09\u5206\u6cd5\u7684\u81ea\u52a8\u805a\u7c7b\u7ed3\u679c<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

\u5728\u4e09\u5206\u6cd5\u7684\u7ed3\u679c\u4e2d(\u88684\u3001\u88685\u3001\u88686)\uff0c\u4e09\u79cd\u6a21\u578b\u6790\u51fa\u7684\u7c7b\u76ee2\u90fd\u6bd4\u8f83\u6df7 \u4e71\uff0c\u51fa\u73b0\u4e86\u7ecf\u5b66\u6587\u672c(\u5982\u300a\u6613\u4f20\u300b\u7b49)\u3001\u53f2\u4e66(\u300a\u53f2\u8bb0\u300b\u7b49)\u3001\u5b57\u4e66(\u300a\u5c14\u96c5\u300b \u7b49)\u3001\u65b9\u672f\u4e66(\u300a\u9ec4\u5e1d\u5185\u7ecf\u300b\u7b49)\u4e0e\u8f9e\u8d4b(\u300a\u5b54\u81e7\u8d4b\u7b49\u300b)\u6df7\u6742\u7684\u73b0\u8c61\u3002\u76f8\u5bf9\u800c\u8a00\uff0cGaussian Mixture Model\u6df7\u5165\u7684\u8f9e\u8d4b\u8f83\u5c11\uff0c\u53ea\u6709\u300a\u5b54\u81e7\u8d4b\u300b\u4e00\u79cd\uff0c\u4f46\u5176\u4ed6\u6587\u732e\u6df7 \u6742\u7684\u73b0\u8c61\u4ecd\u7136\u8f83\u4e3a\u660e\u663e\u3002<\/p>\n\n\n\n

\u88687 K-means++\u56db\u5206\u6cd5\u7684\u81ea\u52a8\u805a\u7c7b\u7ed3\u679c<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

\u88688 Gaussian Mixture Model\u56db\u5206\u6cd5\u7684\u81ea\u52a8\u805a\u7c7b\u7ed3\u679c<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

\u88689 Spectral Clustering\u56db\u5206\u6cd5\u7684\u81ea\u52a8\u805a\u7c7b\u7ed3\u679c<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

\u5c06\u6587\u672c\u7c07\u589e\u52a0\u5230\u56db\u4e2a\u65f6(\u88687\u3001\u88688\u3001\u88689)\uff0cK-means++\u548cSpectral Clustering \u90fd\u660e\u663e\u628a\u300a\u6625\u79cb\u300b\u4e09\u4f20\u3001\u300a\u53f2\u8bb0\u300b\u7b49\u53f2\u7c4d\u7c7b\u6790\u51fa\u4e3a\u65b0\u7684\u7c7b\u76ee 3\u3002[14]<\/sup>\u800c\u5728\u53f2\u7c4d\u7c7b\u6790\u51fa \u540e\uff0c\u539f\u672c\u7ecf\u53f2\u6df7\u6742\u7684\u7c7b\u76ee 2 \u5c31\u5448\u73b0\u51fa\u4ee5\u300a\u5c14\u96c5\u300b\u300a\u5c0f\u5c14\u96c5\u300b\u7b49\u5c0f\u5b66\u4e66\u7c4d\u4e0e\u300a\u53f8\u9a6c\u76f8 \u5982\u8d4b\u300b\u300a\u626c\u96c4\u8d4b\u300b\u7b49\u6c49\u5927\u8d4b\u5e76\u5217\u7684\u683c\u5c40\u3002\u6c49\u5927\u8d4b\u56e0\u4e3a\u559c\u597d\u6392\u6bd4\u540d\u7269\uff0c\u5c24\u5176\u662f\u6392\u6bd4 \u76f8\u540c\u504f\u65c1\u7684\u6c49\u5b57\uff0c\u5176\u6587\u672c\u7279\u5f81\u76f8\u8f83\u4e8e\u300a\u695a\u8f9e\u300b\u300a\u5b8b\u7389\u8d4b\u300b\u7b49\u6587\u5b66\u6027\u66f4\u5f3a\u7684\u9a9a\u8d4b\u800c \u8a00\uff0c\u53cd\u800c\u66f4\u63a5\u8fd1\u4e8e\u5c0f\u5b66\u7c7b\u7684\u5b57\u4e66\uff0c\u805a\u7c7b\u7ed3\u679c\u7b26\u5408\u6b64\u7c7b\u6587\u732e\u7684\u6587\u672c\u7279\u5f81\u3002\u800c\u7c7b\u76ee 1 \u7684\u5b66\u672f\u7c7b\u6587\u732e\uff0c\u5728\u56db\u5206\u6cd5\u65f6\u4e5f\u66f4\u96c6\u4e2d\u4e8e\u8bf8\u5b50\u4e00\u7c7b\u3002Gaussian Mixture Model\u7684\u5206 \u7c7b\u6548\u679c\u4ecd\u4e0d\u7406\u60f3\uff0c\u53f2\u7c4d\u7684\u7c7b\u76ee\u4e2d\u4f9d\u7136\u6df7\u6709\u300a\u793c\u8bb0\u300b\u300a\u8bba\u8bed\u300b\u300a\u5b5f\u5b50\u300b\u300a\u6bdb\u8bd7\u6545\u8bad\u4f20\u300b\u300a\u5b5d\u7ecf\u300b\u7b49\u3002<\/p>\n\n\n\n

\u886810 K-means++\u4e94\u5206\u6cd5\u7684\u81ea\u52a8\u805a\u7c7b\u7ed3\u679c<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

\u886811 Gaussian Mixture Model\u4e94\u5206\u6cd5\u7684\u81ea\u52a8\u805a\u7c7b\u7ed3\u679c<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

\u886812 Spectral Clustering\u4e94\u5206\u6cd5\u7684\u81ea\u52a8\u805a\u7c7b\u7ed3\u679c<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

\u7c7b\u4f3cSpectral Clustering\u51fa\u73b0\u300a\u6dee\u5357\u5c0f\u5c71\u8d4b\u300b\u5355\u72ec\u805a\u7c7b\u7684\u73b0\u8c61\uff0cK-means++\u7684K\u503c\u53d65\u65f6\uff0c\u4e5f\u53ea\u628a\u300a\u6c49\u9ad8\u7956\u8bd7\u300b\u6790\u51fa\uff0c\u56e0\u6b64\u4e94\u5206\u6cd5\u65f6K-means++\u548cSpectral Clustering \u7684K\u503c\u9700\u8981\u53d6\u52306(\u886810\u3001\u886811\u3001\u886812)\u3002\u4e24\u79cd\u6a21\u578b\u90fd\u5728\u56db\u5206\u6cd5\u65f6\u5f62\u6210\u76f8\u5bf9\u7a33\u5b9a\u7684\u7ed3\u679c\uff0c\u4e14\u5927\u4f53\u4e00\u81f4\uff0c\u6709\u7406\u7531\u76f8\u4fe1\u5bf9\u4e8e\u65e9\u671f\u6587\u732e\u6765\u8bf4\uff0c\u56db\u5206\u6cd5\u786e\u662f\u4e00\u79cd\u8f83\u4e3a\u7a33\u5b9a\u7684\u5206\u7c7b\u65b9\u5f0f\uff0c\u5c24\u5176\u662f\u5728\u5904\u7406\u8a00\u8bf4\u7279\u5f81\u65b9\u9762\u5177\u6709\u5185\u5728\u7684\u5408\u7406\u6027\u3002\u800c\u5728\u4e94\u5206\u6cd5\u4e0b\uff0cK-means++\u5c06\u300a\u5468\u6613\u300b\u4ece\u8bf8\u5b50\u6587\u732e\u4e2d\u8c03\u6574\u5230\u7ecf\u4e66\u7c7b\uff0c\u4f7f\u5f97\u8bf8\u5b50\u7c7b\u6587\u732e\u7684\u7acb\u8a00\u6210\u8bf4\u6027\u8d28\u66f4\u52a0\u660e\u663e\u3002\u540c\u65f6\uff0c\u300a\u53f8\u9a6c\u76f8\u5982\u8d4b\u300b\u300a\u626c\u96c4\u8d4b\u300b\u7b49\u4ece\u7ecf\u4e66\u7c7b\u4e2d\u5206\u51fa\uff0c\u6210\u4e3a\u65b0\u7684\u90e8\u7c7b(\u300a\u8bd7\u7ecf\u300b\u4e5f\u88ab\u8c03\u6574\u5230\u8fd9\u4e00\u6587\u5b66\u6027\u66f4\u7a81\u51fa\u7684\u90e8\u7c7b)\uff0c\u7ecf\u4e66\u7c7b\u6587\u732e\u4fbf\u96c6\u4e2d\u4e8e\u7ecf\u4f20\u8bad\u8bc2\u4e0e\u5c0f\u5b66\u65b9\u9762\u3002Spectral Clustering\u7684\u8c03\u6574\u662f\u628a\u300a\u5c14\u96c5\u300b\u7b49\u5c0f\u5b66\u6587\u732e\u6790\u51fa\u4e3a\u4e00\u7c7b\uff0c\u8fd9\u79cd\u805a\u7c7b\u660e\u663e\u662f\u4ee5\u5185\u5bb9\u4e3a\u4f9d\u636e\u7684\u7ed3\u679c\u3002\u7c7b\u4f3c\u5730\uff0cGaussian Mixture Model\u7684\u4e94\u5206\u6cd5\u4e2d\u51fa\u73b0\u4e86\u7c7b\u76ee2\u8fd9\u6279\u8f83\u4e3a\u96c6\u4e2d\u5730\u4f53\u73b0\u5112\u5bb6\u6587\u5316\u89c2\u5ff5\u7684\u6587\u732e\uff0c\u6bd4\u5982\u300a\u5de6\u4f20\u300b\u867d\u7cfb\u53f2\u7c4d\uff0c\u4f46\u5176\u4e2d\u6709\u5f88\u591a\u201c\u541b\u5b50\u66f0\u201d\u7684\u4f53\u73b0\u5fb7\u6027\u7684\u5185\u5bb9\u3002\u53ef\u89c1\uff0cGaussian Mixture Model\u548cSpectral Clustering\u90fd\u5f00\u59cb\u51fa\u73b0\u57fa\u4e8e\u6587\u672c\u5185\u5bb9\u800c\u975e\u8a00\u8bf4\u65b9\u5f0f\u7684\u805a\u7c7b\u7ed3\u679c\uff0c\u5f53\u7136\u8fd9\u4e5f\u9020\u6210\u4e86\u4ece\u8a00\u8bf4\u65b9\u5f0f\u4e0a\u770b\uff0c\u7ecf\u3001\u53f2\u3001\u5b50\u518d\u5ea6\u6742\u7cc5\u7684\u73b0\u8c61\u3002<\/p>\n\n\n\n

\u516d\u5206\u6cd5\u6846\u67b6\u4e0b\uff0c\u4e09\u79cd\u6a21\u578b\u90fd\u628a\u300a\u5c14\u96c5\u300b\u7b49\u56db\u79cd\u5c0f\u5b66\u6587\u732e\u72ec\u7acb\u4e3a\u4e00\u7c7b(\u886813\u3001\u886814\u3001\u886815)\u3002\u800c\u7c7b\u76ee2\u7684\u7ecf\u4e66\u7c7b\u5728\u5206\u51fa\u4e86\u5c0f\u5b66\u6587\u732e\u540e\uff0c\u4e09\u79cd\u6a21\u578b\u7684\u805a\u7c7b\u7ed3\u679c\u90fd\u6216\u591a\u6216\u5c11\u51fa\u73b0\u4e86\u4e0e\u53f2\u7c4d\u3001\u65b9\u6280\u4e66\u7c4d\u76f8\u6df7\u7684\u73b0\u8c61\u3002\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u65e2Gaussian Mixture Model\u4e4b\u540e\uff0cK-means++\u548cSpectral Clustering\u4e5f\u51fa\u73b0\u4e86\u7c7b\u76ee3\u8fd9\u6837\u96c6\u4e2d\u53cd\u6620\u5112\u5bb6\u91cd\u5fb7\u89c2\u5ff5\u7684\u6587\u732e\u805a\u7c7b\uff0c\u4e09\u79cd\u805a\u7c7b\u6a21\u578b\u7684\u7ed3\u679c\u518d\u6b21\u8d8b\u540c\u3002(Spectral Clustering\u7a0d\u900a\u4e00\u7b79\uff0c\u5176\u4e2d\u6df7\u5165\u4e86\u300a\u5e84\u5b50\u300b\u7b49\u9053\u5bb6\u5178\u7c4d\u3002)\u800c\u4e09\u79cd\u6a21\u578b\u4e2d\uff0c\u6c49\u5927\u8d4b\u4e0e\u9a9a\u8d4b\u7684\u805a\u7c7b\u7ed3\u679c\u5728\u6574\u4f53\u4e0a\u4e5f\u6ca1\u6709\u592a\u5927\u53d8\u5316\u3002\u7efc\u62ec\u8a00\u4e4b\uff0c\u5728\u805a\u7c7b\u65b9\u5f0f\u4e0a\u5f62\u6210\u4e86\u5112\u5bb6\u6587\u732e\u3001\u5c0f\u5b66\u6587\u732e\u3001\u6c49\u5927\u8d4b\u3001\u9a9a\u8d4b\u7684\u6587\u672c\u533a\u5206\uff0c\u663e\u793a\u51fa\u4ee5\u6587\u672c\u5185\u5bb9\u4f5c\u4e3a\u805a\u7c7b\u4f9d\u636e\u7684\u53ef\u80fd\u6027\u3002<\/p>\n\n\n\n

\u886813 K-means++\u516d\u5206\u6cd5\u7684\u81ea\u52a8\u805a\u7c7b\u7ed3\u679c<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

\u886814 Gaussian Mixture Model\u516d\u5206\u6cd5\u7684\u81ea\u52a8\u805a\u7c7b\u7ed3\u679c<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

\u886815 Spectral Clustering\u516d\u5206\u6cd5\u7684\u81ea\u52a8\u805a\u7c7b\u7ed3\u679c<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

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<\/p>\n\n\n\n

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\u4e09\u3001\u76ee\u5f55\u4f55\u4ee5\u201c\u4e3b\u4e49\u201d?<\/strong><\/p>\n\n\n\n

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———————————————————————————————————————————————————————————————————<\/p>\n\n\n\n

Re-examination of the Catalogue Classification of Hanshu Yiwenzhi<\/em><\/span><\/strong><\/p>\n\n\n\n

 <\/strong>Zhu Yuchen, Li Shen<\/p>\n\n\n\n

Abstract:<\/strong> In order to investigate the multiple possibilities of literature classification in Hanshu Yiwenzhi, this paper uses the K-means++, Gaussian Mixture Model and Spectral Clustering model to automatically cluster the surviving documents in Hanshu Yiwenzhi. The clustering results show that the clustering from four to six classifications is stable and effective. There are two kinds of classification methods based on the principle of expression and content, which provide more possibilities to beyond Hanzhi. Under the framework of four-part classification, later generations examined the six division of Hanzhi and put forward such propositions as \u201cwhy the historical department is not established\u201d, which also shows the subtle influence of the four-part classification in the construction of knowledge system. In ancient culture, bibliography has gone beyond simple classification catalogue and become a thinking mode with potential influence.<\/p>\n\n\n\n

Keywords:<\/strong> Hanshu Yiwenzhi; QiLue; Classification; Automatically Cluster<\/p>\n\n\n\n

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 \u7f16 \u8f91  |  \u738b\u6ce2<\/p>\n\n\n\n

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*\u672c\u6587\u7cfb\u56fd\u5bb6\u793e\u79d1\u57fa\u91d1\u91cd\u5927\u9879\u76ee\u201c\u57fa\u4e8e\u5927\u6570\u636e\u6280\u672f\u7684\u53e4\u4ee3\u6587\u5b66\u7ecf\u5178\u6587\u672c\u5206\u6790\u4e0e\u7814\u7a76\u201d(18ZDA238)\u9636\u6bb5\u6027\u6210\u679c\u3002<\/p>\n\n\n\n

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\u6ce8\u91ca\uff1a<\/span><\/strong><\/p>\n\n\n\n

[1]\u7ae0\u5b66\u8bda\u8457\uff0c\u53f6\u745b\u6821\u6ce8:\u300a\u6587\u53f2\u901a\u4e49\u6821\u6ce8\u300b\uff0c\u5317\u4eac:\u4e2d\u534e\u4e66\u5c40\uff0c2014\u5e74\uff0c\u7b2c1101\u9875\u3002<\/p>\n\n\n\n

[2]\u53c2\u89c1\u9093\u9a8f\u6377:\u300a\u201c\u8bf8\u5b50\u51fa\u4e8e\u738b\u5b98\u201d\u8bf4\u4e0e\u6c49\u5bb6\u5b66\u672f\u8bdd\u8bed\u300b\uff0c\u300a\u4e2d\u56fd\u793e\u4f1a\u79d1\u5b66\u300b2017\u5e74\u7b2c9\u671f;\u6bb5\u607a\u3001\u7ae5\u5e86\u70b3:\u300a\u8bba\u3008\u6c49\u4e66\u00b7\u827a\u6587\u5fd7\u3009\u7684\u5112\u5b66\u8bdd\u8bed\u5efa\u6784\u300b\uff0c\u300a\u5c71\u897f\u5927\u5b66\u5b66\u62a5\u300b(\u54f2\u5b66\u793e\u4f1a\u79d1\u5b66\u7248)2014\u5e74\u7b2c2\u671f;\u6768\u65b0\u5bbe:\u300a\u76ee\u5f55\u5b66\u4e0e\u5b66\u672f\u53f2\u4e4b\u95f4\u2014\u2014\u3008\u6c49\u4e66\u00b7\u827a\u6587\u5fd7\u3009\u4ef7\u503c\u7684\u518d\u601d\u8003\u300b\uff0c\u300a\u7406\u8bba\u6708\u520a\u300b2012\u5e74\u7b2c6\u671f;\u5f90\u5efa\u59d4:\u300a\u5468\u79e6\u6c49\u6587\u5b66\u7814\u7a76\u4e2d\u7684\u3008\u6c49\u5fd7\u3009\u4e3b\u4e49\u53ca\u5176\u8d85\u8d8a\u300b\uff0c\u300a\u6587\u5b66\u9057\u4ea7\u300b2017\u5e74\u7b2c2\u671f\u3002<\/p>\n\n\n\n

[3]\u7ae0\u5b66\u8bda\u8457\uff0c\u53f6\u745b\u6821\u6ce8:\u300a\u6587\u53f2\u901a\u4e49\u6821\u6ce8\u300b\uff0c\u7b2c1114\u9875\u3002<\/p>\n\n\n\n

[4]\u5f20\u6d9b:\u300a\u3008\u4e03\u7565\u3009\u4e2d\u53f2\u7c4d\u672a\u80fd\u72ec\u7acb\u6210\u90e8\u7c7b\u7684\u6839\u672c\u539f\u56e0\u300b\uff0c\u300a\u6587\u53f2\u54f2\u300b1992\u5e74\u7b2c6\u671f\u3002<\/p>\n\n\n\n

[5]\u9648\u521a:\u300a\u3008\u6c49\u4e66\u00b7\u827a\u6587\u5fd7\u00b7\u8bd7\u8d4b\u7565\u3009\u8d4b\u4e4b\u5206\u7c7b\u7814\u7a76\u8ff0\u7565\u300b\uff0c\u300a\u6587\u732e\u300b2011\u5e74\u7b2c2\u671f\u3002<\/p>\n\n\n\n

[6]\u5f90\u5efa\u59d4:\u300a\u5468\u79e6\u6c49\u6587\u5b66\u7814\u7a76\u4e2d\u7684\u3008\u6c49\u5fd7\u3009\u4e3b\u4e49\u53ca\u5176\u8d85\u8d8a\u300b\uff0c\u300a\u6587\u5b66\u9057\u4ea7\u300b2017\u5e74\u7b2c2\u671f\u3002<\/p>\n\n\n\n

[7]Zellig Harris, \u201cDistributional Structure,\u201d in Word, vol. 10, 1954, pp. 146-162.<\/p>\n\n\n\n

[8]Tomas Mikolov et al., \u201cDistributed Representations of Words and Phrases and their Compositionality,\u201d Proceedings of NIPS, Lake Tahoe, USA: Neural Information Processing Systems Foundation, 2013, pp. 3111-3119.<\/p>\n\n\n\n

[9]Shen Li et al.\u201c, Analogical Reasoning on Chinese Morphological and Semantic Relations,\u201d Proceedings of ACL, Melbourne, Australia: Association for Computational Linguistics, 2018, pp. 138-143.<\/p>\n\n\n\n

[10]David Arthur, Sergei Vassilvitskii, \u201ck-means++: The Advantages of Careful Seeding,\u201d in Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, Philadelphia, PA, USA: Society for Industrial and Applied Mathematics, 2007, pp. 1027-1035.<\/p>\n\n\n\n

[11]Douglas Reynolds\u201c, Gaussian Mixture Models,\u201d Encyclopedia of Biometrics, vol. 741, 2009, pp. 659-663.<\/p>\n\n\n\n

[12]Stella X. Yu, Jianbo Shi, \u201cMulticlass Spectral Clustering,\u201d Proceedings Ninth IEEE International Conference on Computer Vision, Nice, France, 2003, pp. 313-319.<\/p>\n\n\n\n

[13]James MacQueen\u201c, Some Methods for Classification and Analysis of Multivariate Observations,\u201d Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, 1967, pp. 281-297.<\/p>\n\n\n\n

[14]\u7531\u4e8eK\u53d64\u62165\u65f6\uff0cSpectral Clustering\u4f1a\u628a\u300a\u6dee\u5357\u5c0f\u5c71\u8d4b\u300b\u5355\u72ec\u805a\u4e3a\u4e00\u7c7b\uff0c\u6587\u672c\u91cf\u8fc7\u5c0f\uff0c\u5f88\u96be\u8bf4\u5f62\u6210\u7684\u7c7b\u6709\u4f55\u610f\u4e49\uff0c\u56e0\u6b64\u5bf9\u56db\u5206\u6cd5\u7684\u8003\u5bdf\u9700\u8981K\u503c\u53d65\u3002<\/p>\n\n\n\n

[15]\u962e\u5143:\u300a\u63c5\u7ecf\u5ba4\u96c6\u300b\uff0c\u5317\u4eac:\u4e2d\u534e\u4e66\u5c40\uff0c1993\u5e74\uff0c\u7b2c388\u30011071\u9875\u3002<\/p>\n\n\n\n

[16]\u5218\u5e08\u57f9:\u300a\u4e2d\u56fd\u4e2d\u53e4\u6587\u5b66\u53f2\u00b7\u6c49\u9b4f\u516d\u671d\u4e13\u5bb6\u6587\u7814\u7a76\u300b\uff0c\u5317\u4eac:\u5546\u52a1\u5370\u4e66\u9986\uff0c2016\u5e74\uff0c\u7b2c170\u9875\u3002<\/p>\n\n\n\n

[17]\u5218\u5e08\u57f9:\u300a\u4e2d\u56fd\u4e2d\u53e4\u6587\u5b66\u53f2\u00b7\u6c49\u9b4f\u516d\u671d\u4e13\u5bb6\u6587\u7814\u7a76\u300b\uff0c\u7b2c174\u9875\u3002<\/p>\n\n\n\n

[18]\u7ae0\u592a\u708e:\u300a\u56fd\u6545\u8bba\u8861\u300b\uff0c\u5317\u4eac:\u5546\u52a1\u5370\u4e66\u9986\uff0c2012\u5e74\uff0c\u7b2c128\u9875\u3002<\/p>\n\n\n\n

[19]\u90ed\u82f1\u5fb7:\u300a\u3008\u56db\u5e93\u5168\u4e66\u603b\u76ee\u3009\u4e0e\u4e2d\u534e\u5b66\u672f\u4f53\u7cfb\u7684\u6784\u5efa\u300b\uff0c\u300a\u65af\u6587\u300b2017\u5e74\u7b2c1\u8f91\u3002<\/p>\n\n\n\n

[20]\u7ae0\u5b66\u8bda\u8457\uff0c\u53f6\u745b\u6821\u6ce8:\u300a\u6587\u53f2\u901a\u4e49\u6821\u6ce8\u300b\uff0c\u7b2c1127\u9875\u3002<\/p>\n\n\n\n

[21]\u53c2\u89c1\u738b\u6c4e\u68ee:\u300a\u5bf9\u3008\u6587\u53f2\u901a\u4e49\u00b7\u8a00\u516c\u3009\u7684\u4e00\u4e2a\u65b0\u8ba4\u8bc6\u300b\uff0c\u300a\u6743\u529b\u7684\u6bdb\u7ec6\u7ba1\u4f5c\u7528:\u6e05\u4ee3\u7684\u5b66\u672f\u3001\u601d\u60f3\u4e0e\u5fc3\u6001\u300b\uff0c\u5317\u4eac:\u5317\u4eac\u5927\u5b66\u51fa\u7248\u793e\uff0c2015\u5e74\uff0c\u7b2c447\u2014452\u9875\u3002<\/p>\n\n\n\n

[22]\u7ae0\u5b66\u8bda\u8457\uff0c\u53f6\u745b\u6821\u6ce8:\u300a\u6587\u53f2\u901a\u4e49\u6821\u6ce8\u300b\uff0c\u7b2c1179\u9875\u3002<\/p>\n\n\n\n

[23]\u674e\u6148\u94ed:\u300a\u8d8a\u7f26\u5802\u8bfb\u4e66\u8bb0\u300b\uff0c\u5317\u4eac:\u4e2d\u534e\u4e66\u5c40\uff0c1963\u5e74\uff0c\u7b2c782\u9875\u3002<\/p>\n\n\n\n

[24]\u7ae0\u5b66\u8bda\u8457\uff0c\u53f6\u745b\u6821\u6ce8:\u300a\u6587\u53f2\u901a\u4e49\u6821\u6ce8\u300b\uff0c\u7b2c1125\u9875\u3002<\/p>\n\n\n\n

[25]\u73ed\u56fa:\u300a\u6c49\u4e66\u00b7\u695a\u5143\u738b\u4f20\u300b\uff0c\u5317\u4eac:\u4e2d\u534e\u4e66\u5c40\uff0c1962\u5e74\uff0c\u7b2c1967\u9875\u3002<\/p>\n\n\n\n

[26]\u949f\u8087\u9e4f:\u300a\u4e03\u7565\u522b\u5f55\u8003\u300b\uff0c\u300a\u6587\u732e\u300b1985\u5e74\u7b2c3\u671f\u3002<\/p>\n\n\n\n

[27]\u5434\u6c82\u6f90:\u300a\u5218\u5411\u3001\u5218\u6b46\u6821\u4e66\u95ee\u9898\u8003\u8ff0\u300b\uff0c\u300a\u65af\u6587\u300b2018\u5e74\u7b2c3\u8f91\u3002<\/p>\n\n\n\n

[28]\u59da\u540d\u8fbe:\u300a\u4e2d\u56fd\u76ee\u5f55\u5b66\u53f2\u300b\uff0c\u5317\u4eac:\u5546\u52a1\u5370\u4e66\u9986\uff0c2017\u5e74\uff0c\u7b2c41\u9875\u3002<\/p>\n\n\n\n

[29]\u73ed\u56fa:\u300a\u6c49\u4e66\u300b\uff0c\u7b2c1701\u9875\u3002<\/p>\n\n\n\n

[30]\u5982\u5f20\u6d9b\u63d0\u51fa\u6c49\u4ee3\u53f2\u5b66\u6210\u4e3a\u7ecf\u5b66\u9644\u5eb8\u624d\u662f\u300a\u4e03\u7565\u300b\u4e2d\u53f2\u7c4d\u672a\u80fd\u72ec\u7acb\u6210\u90e8\u7c7b\u7684\u6839\u672c\u539f\u56e0\u3002\u53c2\u89c1\u5f20\u6d9b:\u300a\u3008\u4e03\u7565\u3009\u4e2d\u53f2\u7c4d\u672a\u80fd\u72ec\u6210\u90e8\u7c7b\u7684\u6839\u672c\u539f\u56e0\u300b\uff0c\u300a\u6587\u53f2\u54f2\u300b1992\u5e74\u7b2c6\u671f\u3002<\/p>\n\n\n\n

[31]\u4e25\u53ef\u5747\u8f91:\u300a\u5168\u4e0a\u53e4\u4e09\u4ee3\u79e6\u6c49\u4e09\u56fd\u516d\u671d\u6587\u300b\uff0c\u5317\u4eac:\u4e2d\u534e\u4e66\u5c40\uff0c1958\u5e74\uff0c\u7b2c6691\u9875\u3002<\/p>\n\n\n\n

[32]\u7ae0\u5b66\u8bda\u8457\uff0c\u53f6\u745b\u6821\u6ce8:\u300a\u6587\u53f2\u901a\u4e49\u6821\u6ce8\u300b\uff0c\u7b2c1114\u9875\u3002<\/p>\n\n\n\n

[33]\u4f59\u5609\u9521:\u300a\u76ee\u5f55\u5b66\u53d1\u5fae\u300b\uff0c\u5317\u4eac:\u4e2d\u534e\u4e66\u5c40\uff0c2007\u5e74\uff0c\u7b2c144\u2014145\u3001168\u9875\u3002<\/p>\n\n\n\n

[34]\u59da\u540d\u8fbe:\u300a\u4e2d\u56fd\u76ee\u5f55\u5b66\u53f2\u300b\uff0c\u7b2c59\u201460\u9875\u3002<\/p>\n\n\n\n

[35]\u4ee5\u4e0a\u6570\u5b57\uff0c\u51fa\u4e8e\u73ed\u56fa\u7edf\u8ba1\u3002\u53c2\u89c1\u9648\u56fd\u5e86:\u300a\u6c49\u4e66\u827a\u6587\u5fd7\u6ce8\u91ca\u6c47\u7f16\u300b\uff0c\u5317\u4eac:\u4e2d\u534e\u4e66\u5c40\uff0c1983\u5e74\u3002<\/p>\n\n\n\n

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