{"id":4897,"date":"2020-07-21T17:39:21","date_gmt":"2020-07-21T09:39:21","guid":{"rendered":"https:\/\/www.59xuexi.com\/?p=4897"},"modified":"2020-07-21T17:54:07","modified_gmt":"2020-07-21T09:54:07","slug":"hcie-big-data-data-mining-v2-0-%e8%80%83%e8%af%95%e8%ae%a4%e8%af%81%e4%bb%8b%e7%bb%8d","status":"publish","type":"post","link":"https:\/\/www.59xuexi.com\/?p=4897","title":{"rendered":"HCIE-Big Data-Data Mining V2.0 \u8003\u8bd5\u8ba4\u8bc1\u4ecb\u7ecd"},"content":{"rendered":"

HCIE-Big Data-Data Mining V2.0 <\/b>\u8003\u8bd5\u5927\u7eb2<\/h1>\n
\u8003\u8bd5\u6982\u8ff0<\/h2>\n\n\n\n\n\n\n
\u00a0\u8ba4\u8bc1\u540d\u79f0<\/td>\n \u8003\u8bd5\u4ee3\u7801<\/td>\n \u8003\u8bd5\u540d\u79f0<\/td>\n \u8003\u8bd5\u8bed\u8a00<\/td>\n \u8003\u8bd5\u8d39\u7528<\/td>\n \u8003\u8bd5\u65f6\u957f<\/td>\n \u901a\u8fc7\u5206\u6570\/ \u603b\u5206<\/td>\n<\/tr>\n
HCIE-Big Data-Data Mining<\/td>\n H13-731<\/td>\n HCIE-Big Data-Data Mining V2.0 (Written)<\/td>\n CHS<\/td>\n 300 USD<\/td>\n 90min<\/td>\n 600\/1000<\/td>\n<\/tr>\n
<\/td>\n H13-732<\/td>\n HCIE-Big Data-Data Mining V2.0 (Lab)<\/td>\n <\/td>\n 480min<\/td>\n 80\/100<\/td>\n<\/tr>\n
<\/td>\n H13-733<\/td>\n HCIE-Big Data-Data Mining V2.0 (Interview)<\/td>\n <\/td>\n 60min<\/td>\n \u901a\u8fc7\/\u4e0d\u901a\u8fc7<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
$\"H13-731\"$ <\/h2>\n
\u8003\u8bd5\u8303\u56f4<\/h2>\n
\u534e\u4e3a\u8ba4\u8bc1HCIE-Big Data-Data Mining V2.0 \u8003\u8bd5\u8986\u76d6\uff1a\u6570\u636e\u6316\u6398\u4ecb\u7ecd\u3001\u9884\u5907\u77e5\u8bc6\uff08\u6570\u5b66\u57fa\u7840\u77e5\u8bc6\u3001Python \u57fa\u7840\u77e5\u8bc6\uff09\u3001\u6570\u636e\u9884\u5904\u7406\u3001\u7279\u5f81\u9009\u62e9\u4e0e\u964d\u7ef4\u3001\u6709\u76d1\u7763\u5b66\u4e60\u3001\u65e0\u76d1\u7763\u5b66\u4e60\u3001\u6a21\u578b\u8bc4\u4f30\u4e0e\u4f18\u5316\u3001\u6570\u636e\u6316\u6398\u7efc\u5408\u5e94\u7528\u3001Spark MLlib \u6570\u636e\u6316\u6398\u3001\u534e\u4e3a\u4e91\u673a\u5668\u5b66\u4e60\u670d\u52a1MLS\u3001FusionInsight Miner\u3001\u5927\u6570\u636e\u67b6\u6784\u548c\u5927\u6570\u636e\u6cbb\u7406\u3001\u5927\u6570\u636e\u6316\u6398\u3002<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n
\u77e5\u8bc6\u70b9<\/p>\n<\/td>\n
\n
\u7b14\u8bd5\u5360\u6bd4<\/p>\n<\/td>\n
\n
\u5b9e\u9a8c\u5360\u6bd4<\/p>\n<\/td>\n
\n
\u9762\u8bd5\u5360\u6bd4<\/p>\n<\/td>\n<\/tr>\n
\n
1.\u6570\u636e\u6316\u6398\u4ecb\u7ecd<\/p>\n<\/td>\n
\n
4%<\/p>\n<\/td>\n
\n
5%<\/p>\n<\/td>\n
\n
5%<\/p>\n<\/td>\n<\/tr>\n
\n
2.\u9884\u5907\u77e5\u8bc6<\/p>\n<\/td>\n
\n
12%<\/p>\n<\/td>\n
\n
5%<\/p>\n<\/td>\n
\n
10%<\/p>\n<\/td>\n<\/tr>\n
\n
3.\u6570\u636e\u9884\u5904\u7406<\/p>\n<\/td>\n
\n
12%<\/p>\n<\/td>\n
\n
15%<\/p>\n<\/td>\n
\n
15%<\/p>\n<\/td>\n<\/tr>\n
\n
4.\u7279\u5f81\u9009\u62e9\u4e0e\u964d\u7ef4<\/p>\n<\/td>\n
\n
7%<\/p>\n<\/td>\n
\n
15%<\/p>\n<\/td>\n
\n
10%<\/p>\n<\/td>\n<\/tr>\n
\n
5.\u6709\u76d1\u7763\u5b66\u4e60<\/p>\n<\/td>\n
\n
11%<\/p>\n<\/td>\n
\n
15%<\/p>\n<\/td>\n
\n
20%<\/p>\n<\/td>\n<\/tr>\n
\n
6.\u65e0\u76d1\u7763\u5b66\u4e60<\/p>\n<\/td>\n
\n
5%<\/p>\n<\/td>\n
\n
15%<\/p>\n<\/td>\n
\n
15%<\/p>\n<\/td>\n<\/tr>\n
\n
7.\u6a21\u578b\u8bc4\u4f30\u4e0e\u4f18\u5316<\/p>\n<\/td>\n
\n
12%<\/p>\n<\/td>\n
\n
25%<\/p>\n<\/td>\n
\n
5%<\/p>\n<\/td>\n<\/tr>\n
\n
8.\u6570\u636e\u6316\u6398\u7efc\u5408\u5e94\u7528<\/p>\n<\/td>\n
\n
8%<\/p>\n<\/td>\n
\n
5%<\/p>\n<\/td>\n
\n
5%<\/p>\n<\/td>\n<\/tr>\n
\n
9.Spark MLlib \u6570\u636e\u6316\u6398<\/p>\n<\/td>\n
\n
11%<\/p>\n<\/td>\n
\n
0%<\/p>\n<\/td>\n
\n
10%<\/p>\n<\/td>\n<\/tr>\n
\n
10.\u534e\u4e3a\u4e91\u673a\u5668\u5b66\u4e60\u670d\u52a1MLS<\/p>\n<\/td>\n
\n
6%<\/p>\n<\/td>\n
\n
0%<\/p>\n<\/td>\n
\n
0%<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n
\n
11.\u5927\u6570\u636e\u67b6\u6784\u548c\u5927\u6570\u636e\u6cbb\u7406<\/p>\n<\/td>\n
\n
9%<\/p>\n<\/td>\n
\n
0%<\/p>\n<\/td>\n
\n
5%<\/p>\n<\/td>\n<\/tr>\n
\n
12\u5927\u6570\u636e\u6316\u6398<\/p>\n<\/td>\n
\n
3%<\/p>\n<\/td>\n
\n
0%<\/p>\n<\/td>\n
\n
0%<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
\u7b2c\u4e00\u7ae0 \u6570\u636e\u6316\u6398\u4ecb\u7ecd<\/p>\n
1.\u6570\u636e\u6316\u6398\u6982\u8ff0 2.\u6570\u636e\u6316\u6398\u6d41\u7a0b<\/p>\n
3.\u6570\u636e\u3001\u5c5e\u6027\u548c\u5ea6\u91cf<\/p>\n
4.\u6570\u636e\u6316\u6398\u5f00\u53d1\u5de5\u5177<\/p>\n
5.\u6570\u636e\u6316\u6398\u5b66\u4e60\u8def\u5f84<\/p>\n
\u7b2c\u4e8c\u7ae0 \u9884\u5907\u77e5\u8bc6<\/p>\n
1.<\/b>\u77e9\u9635\u548c\u7ebf\u6027\u4ee3\u6570<\/p>\n
1.1 \u884c\u5217\u5f0f<\/p>\n
1.2 \u77e9\u9635\u53ca\u5176\u53d8\u6362<\/p>\n
1.3 \u77e9\u9635\u5206\u89e3<\/p>\n
1.3.1 \u5947\u5f02\u503c\u5206\u89e3<\/p>\n
1.3.2 \u7279\u5f81\u503c\u5206\u89e3<\/p>\n
1.4 \u7ebf\u6027\u53d8\u6362<\/p>\n
1.5 \u5411\u91cf\u7a7a\u95f4<\/p>\n
2 <\/b>\u6982\u7387\u8bba\u548c\u6570\u7406\u7edf\u8ba1<\/p>\n
2.1 \u968f\u673a\u4e8b\u4ef6\u53ca\u5176\u6982\u7387<\/p>\n
2.2 \u968f\u673a\u53d8\u91cf\u53ca\u5176\u5206\u5e03<\/p>\n
2.3 \u968f\u673a\u5411\u91cf\u53ca\u5176\u5206\u5e03<\/p>\n
2.4 \u968f\u673a\u53d8\u91cf\u7684\u51fd\u6570<\/p>\n
2.5 \u968f\u673a\u53d8\u91cf\u7684\u6570\u5b57\u7279\u5f81<\/p>\n
2.6 \u5927\u6570\u5b9a\u5f8b\u4e0e\u4e2d\u5fc3\u6781\u9650\u5b9a\u7406<\/p>\n
2.7 \u53c2\u6570\u4f30\u8ba1<\/p>\n
2.8 \u5047\u8bbe\u68c0\u9a8c<\/p>\n
2.9 \u65b9\u5dee\u5206\u6790\u548c\u56de\u5f52\u5206\u6790<\/p>\n
3 <\/b>\u4fe1\u606f\u71b5\u4e0e\u57fa\u5c3c\u7cfb\u6570<\/p>\n
4 <\/b>\u6700\u4f18\u5316<\/p>\n
4.1 \u65e0\u7ea6\u675f\u6700\u4f18\u5316\u95ee\u9898<\/p>\n
4.2 \u68af\u5ea6\u4e0b\u964d\u6cd5<\/p>\n
4.3 \u7ea6\u675f\u6700\u4f18\u5316\u95ee\u9898<\/p>\n
4.4 \u62c9\u683c\u6717\u65e5\u4e58\u5b50\u6cd5<\/p>\n
5 Python<\/b>\u8bed\u8a00\u57fa\u7840<\/p>\n
5.1 \u4ec0\u4e48\u662fPython<\/p>\n
5.2 Python\u57fa\u7840\u77e5\u8bc6<\/p>\n
5.3 Python\u4e2d\u7684\u6570\u636e\u7c7b\u578b<\/p>\n
5.4 \u5224\u65ad\u4e0e\u5faa\u73af\u8bed\u53e5<\/p>\n
5.5 \u51fd\u6570\u548c\u9762\u5411\u5bf9\u8c61<\/p>\n
5.6 \u5e38\u7528\u6807\u51c6\u5e93<\/p>\n
5.7 \u5e38\u7528\u7b2c\u4e09\u65b9\u5e93<\/p>\n
5.8 \u6b63\u5219\u8868\u8fbe\u5f0f<\/p>\n
5.9 \u6587\u4ef6\u64cd\u4f5c<\/p>\n
6 <\/b>\u6570\u636e\u91c7\u96c6\u4e0e\u722c\u866b<\/p>\n
6.1 \u4ec0\u4e48\u662f\u722c\u866b<\/p>\n
6.2 \u722c\u866b\u7684\u4f5c\u7528\u53ca\u5de5\u4f5c\u6d41\u7a0b<\/p>\n
6.3 \u722c\u866b\u5e38\u7528\u7684\u5de5\u5177<\/p>\n
6.4 \u6570\u636e\u63d0\u53d6\u4e0e\u5b58\u50a8<\/p>\n
6.5 \u5e38\u89c1\u7684\u53cd\u722c\u673a\u5236\u548c\u5e94\u5bf9\u63aa\u65bd<\/p>\n
6.6 \u722c\u866b\u7a0b\u5e8f\u7684\u5b9e\u73b0<\/p>\n
7 <\/b>\u6570\u636e\u53ef\u89c6\u5316<\/p>\n
7.1 \u4ec0\u4e48\u662f\u6570\u636e\u53ef\u89c6\u5316<\/p>\n
7.2 \u6570\u636e\u53ef\u89c6\u5316\u7684\u4f5c\u7528\u53ca\u4f7f\u7528\u573a\u666f<\/p>\n
7.3 \u6570\u636e\u53ef\u89c6\u5316\u7684\u5e38\u7528\u5de5\u5177<\/p>\n
7.4 \u6570\u636e\u53ef\u89c6\u5316\u7684\u5b9e\u73b0\u6d41\u7a0b<\/p>\n
\u7b2c\u4e09\u7ae0 \u6570\u636e\u9884\u5904\u7406<\/p>\n
1 <\/b>\u6570\u636e\u62bd\u53d6\u3001\u8f6c\u6362\u548c\u52a0\u8f7d<\/p>\n
1.1 \u6570\u636e\u62bd\u53d6\u3001\u8f6c\u6362\u548c\u52a0\u8f7d\u6982\u8ff0<\/p>\n
1.2 \u6570\u636e\u62bd\u53d6<\/p>\n
1.3 \u6570\u636e\u8f6c\u6362<\/p>\n
1.4 \u6570\u636e\u52a0\u8f7d<\/p>\n
1.5 ETL\u548cELT\u4ecb\u7ecd<\/p>\n
2 <\/b>\u6570\u636e\u6e05\u6d17<\/p>\n
2.1 \u4e0d\u5747\u8861\u6570\u636e\u5904\u7406<\/p>\n
2.2 \u7f3a\u5931\u503c\u5904\u7406<\/p>\n
2.3 \u5f02\u5e38\u503c\u5904\u7406<\/p>\n
3 <\/b>\u7279\u5f81\u5904\u7406<\/p>\n
3.1 \u7279\u5f81\u7f29\u653e<\/p>\n
3.2 \u6570\u503c\u79bb\u6563\u5316<\/p>\n
3.3 \u7279\u5f81\u7f16\u7801<\/p>\n
3.4 \u65f6\u95f4\u6570\u503c\u8f6c\u6362<\/p>\n
\u7b2c\u56db\u7ae0 \u7279\u5f81\u9009\u62e9\u4e0e\u964d\u7ef4<\/p>\n
1 <\/b>\u7279\u5f81\u9009\u62e9<\/p>\n
1.1 \u7279\u5f81\u9009\u62e9\u6982\u8ff0<\/p>\n
1.2 Filter<\/p>\n
1.3 Wrapper<\/p>\n
1.4 Embedded<\/p>\n
1.5 \u5176\u4ed6\u65b9\u6cd5\u548c\u7279\u5f81\u6269\u589e<\/p>\n
2 <\/b>\u964d\u7ef4<\/p>\n
2.1 \u964d\u7ef4\u5bfc\u5165<\/p>\n
2.2 SVD<\/p>\n
2.3 PCA<\/p>\n
2.4 LDA<\/p>\n
2.5 LLE<\/p>\n
\u7b2c\u4e94\u7ae0 \u6709\u76d1\u7763\u5b66\u4e60<\/p>\n
1 <\/b>\u6709\u76d1\u7763\u5b66\u4e60\u7684\u9884\u5907\u77e5\u8bc6<\/p>\n
1.1 \u673a\u5668\u5b66\u4e60<\/p>\n
1.2 \u673a\u5668\u5b66\u4e60\u5206\u7c7b<\/p>\n
1.3 \u57fa\u672c\u672f\u8bed\u4e0e\u6982\u5ff5<\/p>\n
2 <\/b>\u7ebf\u6027\u56de\u5f52<\/p>\n
2.1 \u57fa\u672c\u6982\u5ff5<\/p>\n
2.2 \u8bef\u5dee<\/p>\n
2.3 \u6b63\u89c4\u65b9\u7a0b<\/p>\n
2.4 \u68af\u5ea6\u4e0b\u964d<\/p>\n
2.5 \u6b63\u5219\u5316<\/p>\n
3 <\/b>\u903b\u8f91\u56de\u5f52<\/p>\n
3.1 \u57fa\u672c\u6982\u5ff5<\/p>\n
3.2 \u76ee\u6807\u51fd\u6570<\/p>\n
3.3 \u635f\u5931\u51fd\u6570<\/p>\n
3.4 \u4f18\u5316\u65b9\u6cd5<\/p>\n
4 KNN <\/b><\/p>\n
4.1 \u57fa\u672c\u6982\u5ff5<\/p>\n
4.2 KNN\u7b97\u6cd5\u4e09\u8981\u7d20<\/p>\n
5 <\/b>\u6734\u7d20\u8d1d\u53f6\u65af<\/p>\n
5.1 \u8d1d\u53f6\u65af\u7b97\u6cd5<\/p>\n
5.2 \u6734\u7d20\u8d1d\u53f6\u65af\u5206\u7c7b\u7b97\u6cd5<\/p>\n
5.3 \u6734\u7d20\u8d1d\u53f6\u65af\u5206\u7c7b\u7b97\u6cd5\u7684\u4f18\u7f3a\u70b9<\/p>\n
6 SVM <\/b><\/p>\n
6.1 \u57fa\u672c\u6982\u5ff5<\/p>\n
6.2 \u7ebf\u6027\u5206\u7c7b<\/p>\n
6.3 \u7ebf\u6027SVM<\/p>\n
6.4 \u975e\u7ebf\u6027\u5206\u7c7b<\/p>\n
6.5 \u975e\u7ebf\u6027SVM<\/p>\n
7 <\/b>\u51b3\u7b56\u6811<\/p>\n
7.1 \u57fa\u672c\u6982\u5ff5<\/p>\n
7.2 ID3<\/p>\n
7.3 C4.5<\/p>\n
7.4 CART<\/p>\n
8 <\/b>\u96c6\u6210\u7b97\u6cd5<\/p>\n
8.1 \u57fa\u672c\u6982\u5ff5<\/p>\n
8.2 \u7ed3\u5408\u7b56\u7565<\/p>\n
8.3 Bagging<\/p>\n
8.4 \u968f\u673a\u68ee\u6797<\/p>\n
8.5 Boosting<\/p>\n
8.6 Adaboost<\/p>\n
8.7 GBDT<\/p>\n
8.8 XGboost<\/p>\n
\u7b2c\u516d\u7ae0 \u65e0\u76d1\u7763\u5b66\u4e60<\/p>\n
1 <\/b>\u65e0\u76d1\u7763\u5b66\u4e60<\/p>\n
1.1 \u65e0\u76d1\u7763\u5b66\u4e60\u6982\u5ff5\u4e0e\u5bfc\u5165<\/p>\n
2 <\/b>\u805a\u7c7b\u7b97\u6cd5<\/p>\n
2.1 \u805a\u7c7b\u5206\u6790\u6982\u5ff5<\/p>\n
2.2 \u57fa\u4e8e\u539f\u578b\u805a\u7c7b<\/p>\n
2.2.1 K-Means\u7b97\u6cd5<\/p>\n
2.2.2 K-Mediods\u7b97\u6cd5<\/p>\n
2.3 \u57fa\u4e8e\u5c42\u6b21\u805a\u7c7b<\/p>\n
2.3.1 Hierarchical Clustering\u7b97\u6cd5<\/p>\n
2.3.2 BIRCH\u7b97\u6cd5<\/p>\n
2.4 \u57fa\u4e8e\u5bc6\u5ea6\u805a\u7c7b<\/p>\n
2.4.1 DBSCAN\u7b97\u6cd5<\/p>\n
3 <\/b>\u5173\u8054\u7b97\u6cd5<\/p>\n
3.1 Apriori\u7b97\u6cd5<\/p>\n
3.2 FP-growth\u7b97\u6cd5<\/p>\n
\u7b2c\u4e03\u7ae0 \u6a21\u578b\u8bc4\u4f30\u4e0e\u4f18\u5316<\/p>\n
1 <\/b>\u6a21\u578b\u8bc4\u4f30\u4e0e\u4f18\u5316\u9884\u5907\u77e5\u8bc6<\/p>\n
1.1 \u57fa\u672c\u672f\u8bed\u53ca\u6982\u5ff5<\/p>\n
2 <\/b>\u6700\u4f18\u5316\u6a21\u578b<\/p>\n
2.1 \u6700\u4f18\u5316\u6a21\u578b\u7684\u6982\u8ff0<\/p>\n
2.2 \u51f8\u4f18\u5316<\/p>\n
2.3 \u635f\u5931\u51fd\u6570<\/p>\n
2.4 \u6700\u4f18\u5316\u6a21\u578b\u7684\u5206\u7c7b<\/p>\n
3 <\/b>\u6a21\u578b\u8bc4\u4f30\u4e0e\u9009\u62e9<\/p>\n
3.1 \u6a21\u578b\u8bc4\u4f30\u6982\u8ff0<\/p>\n
3.2 \u6570\u636e\u96c6\u62c6\u5206<\/p>\n
3.3 \u56de\u5f52\u6a21\u578b\u8bc4\u4f30<\/p>\n
3.4 \u5206\u7c7b\u6a21\u578b\u8bc4\u4f30<\/p>\n
3.5 \u805a\u7c7b\u6a21\u578b\u8bc4\u4f30<\/p>\n
4 <\/b>\u6b63\u5219\u5316<\/p>\n
\u7b2c\u516b\u7ae0 \u6570\u636e\u6316\u6398\u7efc\u5408\u5e94\u7528<\/p>\n
1 <\/b>\u6570\u636e\u6316\u6398\u7684\u6d41\u7a0b<\/p>\n
1.1 \u6570\u636e\u6316\u6398\u6d41\u7a0b\u6982\u8ff0<\/p>\n
1.2 \u5206\u6790\u9700\u6c42<\/p>\n
1.3 \u6570\u636e\u8bfb\u53d6<\/p>\n
1.4 \u6570\u636e\u9884\u5904\u7406<\/p>\n
1.5 \u7279\u5f81\u5de5\u7a0b<\/p>\n
1.6 \u7279\u5f81\u9009\u62e9<\/p>\n
1.7 \u6a21\u578b\u9009\u62e9<\/p>\n
1.8 \u6a21\u578b\u8bc4\u4f30<\/p>\n
2 <\/b>\u7efc\u5408\u5e94\u7528\u7684\u6848\u4f8b\u5206\u6790<\/p>\n
\u7b2c\u4e5d\u7ae0 Spark MLlib<\/b>\u6570\u636e\u6316\u6398<\/p>\n
1 Spark MLlib<\/b>\u57fa\u7840\u5165\u95e8<\/p>\n
1.1 Spark MLlib\u7b80\u4ecb<\/p>\n
1.2 Spark MLlib\u77e9\u9635\u5411\u91cf<\/p>\n
2 Spark MLlib<\/b>\u57fa\u7840\u7edf\u8ba1\u5206\u6790<\/p>\n
2.1 Basic Statistics \u7b80\u4ecb<\/p>\n
2.2 Summery statistic (\u6c47\u603b\u7edf\u8ba1)<\/p>\n
2.3 Correlations (\u76f8\u5173\u7cfb\u6570)<\/p>\n
2.4 Stratified sampling (\u5206\u5c42\u62bd\u6837)<\/p>\n
2.5 Hypothesis Testing (\u5047\u8bbe\u68c0\u9a8c)<\/p>\n
2.6 Random data generation (\u968f\u673a\u6570\u751f\u6210)<\/p>\n
2.7 Kernel density estimation (\u6838\u5bc6\u5ea6\u4f30\u8ba1)<\/p>\n
3 Spark MLlib<\/b>\u7279\u5f81\u63d0\u53d6\u548c\u8f6c\u6362<\/p>\n
3.1 TF-IDF<\/p>\n
3.2 Word2Vec<\/p>\n
3.3 StandardScaler\uff0cMinMaxScaler\uff0cMaxAbsScaler<\/p>\n
3.4 Normalizer<\/p>\n
3.5 ChiSqSelector<\/p>\n
3.6 ElementwiseProduct<\/p>\n
4 Spark MLlib<\/b>\u5206\u7c7b\u4e0e\u56de\u5f52<\/p>\n
4.1 \u5206\u7c7b\u548c\u56de\u5f52\u7b80\u4ecb<\/p>\n
4.2 \u7ebf\u6027\u6a21\u578b<\/p>\n
4.3 \u51b3\u7b56\u6811\u6a21\u578b<\/p>\n
4.4 \u96c6\u6210\u6a21\u578b<\/p>\n
4.5 \u6734\u7d20\u8d1d\u53f6\u65af\u6a21\u578b<\/p>\n
5 Spark MLlib<\/b>\u805a\u7c7b\u4e0e\u964d\u7ef4<\/p>\n
5.1 \u805a\u7c7b\u7b97\u6cd5\u56de\u987e<\/p>\n
5.2 KMeans\u7b97\u6cd5<\/p>\n
5.3 Spark MLlib\u964d\u7ef4\u7b97\u6cd5\u7b80\u4ecb<\/p>\n
5.4 SVD\u7b97\u6cd5 5.5 PCA\u7b97\u6cd5<\/p>\n
6 Spark MLlib<\/b>\u5173\u8054\u89c4\u5219\u4e0e\u63a8\u8350\u7b97\u6cd5<\/p>\n
6.1 \u5173\u8054\u89c4\u5219\u7b97\u6cd5\u56de\u987e<\/p>\n
6.2 Spark MLlib\u4e2dFP-Growth\u7b97\u6cd5<\/p>\n
6.3 Spark MLlib\u4e2dPrefixSpan\u7b97\u6cd5<\/p>\n
6.4 \u534f\u540c\u8fc7\u6ee4\u7b97\u6cd5\u56de\u987e<\/p>\n
6.5 Spark MLlib\u4e2d\u534f\u540c\u8fc7\u6ee4\u7b97\u6cd5<\/p>\n
7 Spark MLlib<\/b>\u8bc4\u4f30\u77e9\u9635<\/p>\n
7.1 Spark MLlib\u6a21\u578b\u8bc4\u4f30<\/p>\n
7.2 \u5206\u7c7b\u6a21\u578b\u8bc4\u4f30<\/p>\n
7.3 \u56de\u5f52\u6a21\u578b\u8bc4\u4f30<\/p>\n
\u7b2c\u5341\u7ae0 \u534e\u4e3a\u4e91\u673a\u5668\u5b66\u4e60\u670d\u52a1MLS <\/b><\/p>\n
1 <\/b>\u534e\u4e3aMLS<\/b>\u670d\u52a1\u4ecb\u7ecd<\/p>\n
2 <\/b>\u7533\u8bf7\u534e\u4e3aMLS<\/b>\u670d\u52a1<\/p>\n
3 <\/b>\u521b\u5efa\u534e\u4e3aMLS<\/b>\u5de5\u4f5c\u6d41<\/p>\n
4 <\/b>\u5178\u578b\u7b97\u6cd5\u7684\u5e94\u7528<\/p>\n
5 <\/b>\u673a\u5668\u5b66\u4e60\u5e73\u53f0FusionInsight Miner <\/b><\/p>\n
\u7b2c\u5341\u4e00\u7ae0 \u5927\u6570\u636e\u67b6\u6784\u548c\u5927\u6570\u636e\u6cbb\u7406<\/p>\n
1 <\/b>\u5927\u6570\u636e\u67b6\u6784<\/p>\n
1.1 \u5927\u6570\u636e\u67b6\u6784\u6982\u8ff0<\/p>\n
1.2 \u5927\u6570\u636e\u67b6\u6784\u5728\u5927\u6570\u636e\u4e2d\u7684\u91cd\u8981\u6027<\/p>\n
1.3 \u5927\u6570\u636e\u67b6\u6784\u5e08\u6240\u5177\u5907\u7684\u80fd\u529b<\/p>\n
1.4 \u5982\u4f55\u6784\u5efa\u5927\u6570\u636e\u67b6\u6784\u5e73\u53f0<\/p>\n
1.5 \u5927\u6570\u636e\u4e1a\u52a1\u5c42\u901a\u7528\u67b6\u6784<\/p>\n
2 <\/b>\u5927\u6570\u636e\u6cbb\u7406<\/p>\n
2.1 \u5927\u6570\u636e\u6cbb\u7406\u6982\u8ff0<\/p>\n
2.2 \u5927\u6570\u636e\u6cbb\u7406\u5efa\u8bbe\u80cc\u666f\u548c\u76ee\u6807<\/p>\n
2.3 \u4f01\u4e1a\u6570\u636e\u89c4\u5212\u53ca\u6cbb\u7406\u6a21\u578b<\/p>\n
2.4 \u5927\u6570\u636e\u6cbb\u7406\u6848\u4f8b<\/p>\n
\u7b2c\u5341\u4e8c\u7ae0 \u5927\u6570\u636e\u6316\u6398<\/p>\n
1 <\/b>\u6570\u636e\u6316\u6398\u80cc\u666f<\/p>\n
2 <\/b>\u94f6\u884c\u5ba2\u6237\u7cbe\u51c6\u753b\u50cf\u6848\u4f8b<\/p>\n
3 <\/b>\u63d0\u5347\u4fe1\u7528\u5361\u5b89\u5168\u6848\u4f8b<\/p>\n
4 <\/b>\u57ce\u5e02\u73af\u5883\u8d28\u91cf\u5206\u6790\u6316\u6398\u6848\u4f8b<\/p>\n
\u8bf7\u6ce8\u610f\uff1a \u672c\u6587\u63d0\u5230\u7684\u8003\u8bd5\u5185\u5bb9\u4ec5\u4e3a\u8003\u751f\u63d0\u4f9b\u4e00\u4e2a\u901a\u7528\u7684\u8003\u8bd5\u6307\u5f15\uff0c\u672c\u6587\u672a\u63d0\u5230\u7684\u5176\u4ed6\u76f8\u5173\u5185\u5bb9\u5728\u8003\u8bd5\u4e2d\u4e5f\u6709\u53ef\u80fd\u51fa\u73b0\u3002<\/p>\n
\u8be5\u8003\u8bd5\u5927\u7eb2\u662f\u5bf9\u51c6\u5907\u8003\u8bd5\u7684\u8003\u751f\u5728\u62e5\u6709\u591a\u5e74\u5b9e\u9645\u5de5\u4f5c\u7ecf\u9a8c\u7684\u524d\u63d0\u4e0b\u8fdb\u884c\u5907\u8003\u65f6\u7684\u8865\u5145\u3002\u8bf7\u6ce8\u610f\u8fd9\u91cc\u5217\u51fa\u7684\u662fHCIE-Big Data-Data Mining V2.0\u7684\u8003\u8bd5\u5927\u7eb2\uff0c\u4f46\u4e0d\u4ee3\u8868HCIE-Big Data-Data Mining V2.0\u8003\u8bd5\u4e0d\u80fd\u8003\u67e5\u5176\u4ed6\u77e5\u8bc6\u3002\u8be5\u8003\u8bd5\u5927\u7eb2\u4ee3\u8868\u4e86\u6211\u4eec\u8981\u6c42\u8003\u751f\u81f3\u5c11\u8981\u638c\u63e1\u4ee5\u4e0a\u77e5\u8bc6\uff0c\u4f46\u8003\u751f\u5fc5\u987b\u8981\u80fd\u591f\u719f\u7ec3\u5e94\u7528\u8fd9\u4e9b\u77e5\u8bc6\u548c\u76f8\u5173\u77e5\u8bc6\u70b9\uff0c\u624d\u80fd\u901a\u8fc7HCIE-Big Data-Data Mining V2.0\u7684\u8003\u6838\u3002\u6240\u4ee5\u5176\u4ed6\u77e5\u8bc6\u70b9\u4e5f\u53ef\u80fd\u5b58\u5728\u4e8e\u5b9e\u9645\u7684\u8003\u8bd5\u4e2d\u3002\u6211\u4eec\u6b22\u8fce\u5f97\u5230\u60a8\u7684\u5efa\u8bae\u548c\u610f\u89c1\u3002\u60a8\u53ef\u4ee5\u53d1\u9001\u90ae\u4ef6\u7ed9\u6211\u4eec\uff0cEmail: HCIE@huawei.com\u3002<\/p>\n
\u53c2\u8003\u4e66\u7c4d<\/h2>\n
\u534e\u4e3a\u4f01\u4e1a\u5927\u6570\u636e\u6316\u6398\u4e13\u5bb6\u8ba4\u8bc1HCIE-Big Data-Data Mining V2.0 \u57f9\u8bad\u6559\u6750<\/p>\n
\u534e\u4e3a\u4f01\u4e1a\u5927\u6570\u636e\u6316\u6398\u4e13\u5bb6\u8ba4\u8bc1HCIE-Big Data-Data Mining V2.0 \u5b9e\u9a8c\u624b\u518c<\/p>\n
\u534e\u4e3a\u4f01\u4e1a\u5927\u6570\u636e\u5de5\u7a0b\u5e08\u8ba4\u8bc1HCIA-Big Data V2.0 \u57f9\u8bad\u6559\u6750<\/p>\n
\u534e\u4e3a\u4e91EI\uff08https:\/\/www.huaweicloud.com\/ei\uff09\u5404\u670d\u52a1\u7684\u6587\u6863<\/p>\n
\u534e\u4e3a\u4e91\u4ea7\u54c1\u6587\u6863<\/p>\n
\u63a8\u8350\u57f9\u8bad<\/h2>\n
\u534e\u4e3a\u4f01\u4e1a\u5927\u6570\u636e\u6316\u6398\u4e13\u5bb6\u8ba4\u8bc1 HCIE-Big Data-Data Mining V2.0\u57f9\u8bad<\/p>\n
\n
59\u5b66\u4e60\u7f51\uff0c\u52a9\u60a8\u5feb\u901f\u83b7\u53d6IT\u804c\u4e1a\u6280\u80fd\u8bc1\u4e66\uff01\u672c\u6587\u94fe\u63a5\uff1ahttps:\/\/www.59xuexi.com\/?p=4897<\/a><\/p>\n<\/blockquote>\n","protected":false},"excerpt":{"rendered":"
HCIE-Big Data-Data Mining V2.0 \u8003\u8bd5\u5927\u7eb2 \u8003\u8bd5\u6982\u8ff0 \u00a0\u8ba4\u8bc1\u540d\u79f0 \u8003\u8bd5\u4ee3\u7801 \u8003\u8bd5\u540d\u79f0 \u8003\u8bd5\u8bed\u8a00 \u8003\u8bd5\u8d39\u7528 \u8003\u8bd5\u65f6\u957f \u901a\u8fc7\u5206\u6570\/ \u603b\u5206 HCIE-Big Data-Data Mining H13-731 HCIE-…<\/p>\n","protected":false},"author":1,"featured_media":4899,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9],"tags":[679,93,678],"topic":[],"class_list":["post-4897","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-hcie-material","tag-h13-731","tag-hcie","tag-hcie-big-data"],"_links":{"self":[{"href":"https:\/\/www.59xuexi.com\/index.php?rest_route=\/wp\/v2\/posts\/4897","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.59xuexi.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.59xuexi.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.59xuexi.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.59xuexi.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4897"}],"version-history":[{"count":2,"href":"https:\/\/www.59xuexi.com\/index.php?rest_route=\/wp\/v2\/posts\/4897\/revisions"}],"predecessor-version":[{"id":4905,"href":"https:\/\/www.59xuexi.com\/index.php?rest_route=\/wp\/v2\/posts\/4897\/revisions\/4905"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.59xuexi.com\/index.php?rest_route=\/wp\/v2\/media\/4899"}],"wp:attachment":[{"href":"https:\/\/www.59xuexi.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4897"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.59xuexi.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4897"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.59xuexi.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4897"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/www.59xuexi.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftopic&post=4897"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}