\n
public static class Customers{\n \n @RowKey String income;\n String spending_score;\n \n }<\/code><\/pre>\n<\/div>\n\u4e0a\u8a18\u306e\u30af\u30e9\u30b9\u306f\u30012\u3064\u306e\u30ab\u30e9\u30e0\u3092\u6301\u3064\u30b3\u30f3\u30c6\u30ca\u307e\u305f\u306fSQL\u30c6\u30fc\u30d6\u30eb\u3068\u3057\u3066\u898b\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
\u3053\u3053\u3067\u3001GridDB\u3078\u306e\u63a5\u7d9a\u3092\u78ba\u7acb\u3055\u305b\u307e\u3059\u3002\u63a5\u7d9a\u3059\u308b\u30af\u30e9\u30b9\u30bf\u306e\u540d\u524d\u3001\u63a5\u7d9a\u3059\u308b\u5fc5\u8981\u306e\u3042\u308b\u30e6\u30fc\u30b6\u306e\u540d\u524d\u3001\u304a\u3088\u3073\u305d\u306e\u30e6\u30fc\u30b6\u306e\u30d1\u30b9\u30ef\u30fc\u30c9\u306a\u3069\u3001GridDB\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u306e\u4ed5\u69d8\u3092\u4f7f\u7528\u3057\u3066Properties\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u4f5c\u6210\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n
\n
Properties props = new Properties();\n props.setProperty(\"notificationAddress\", \"239.0.0.1\");\n props.setProperty(\"notificationPort\", \"31999\");\n props.setProperty(\"clusterName\", \"defaultCluster\");\n props.setProperty(\"user\", \"admin\");\n props.setProperty(\"password\", \"admin\");\n GridStore store = GridStoreFactory.getInstance().getGridStore(props);<\/code><\/pre>\n<\/div>\n\u4e0a\u8a18\u306f\u3001\u79c1\u305f\u3061\u304c\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u305fGridDB\u306e\u8a73\u7d30\u3067\u3059\u3002\u5404\u81ea\u306e\u74b0\u5883\u306b\u5408\u308f\u305b\u3066\u9069\u5b9c\u5909\u66f4\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n
\u4eca\u56de\u306fCustomers<\/code>\u30b3\u30f3\u30c6\u30ca\u3092\u4f7f\u7528\u3059\u308b\u306e\u3067\u3001\u307e\u305a\u305d\u308c\u3092\u9078\u629e\u3057\u307e\u3059\u3002<\/p>\n\n
Collection<String, Customers> coll = store.putCollection(\"col01\", Customers.class);<\/code><\/pre>\n<\/div>\n\u30b3\u30f3\u30c6\u30ca Customers<\/code> \u306e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u4f5c\u6210\u3057\u3001coll<\/code> \u3068\u3044\u3046\u540d\u524d\u3092\u4ed8\u3051\u307e\u3057\u305f\u3002\u4eca\u5f8c\u306f\u3053\u306e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u4f7f\u3063\u3066\u30b3\u30f3\u30c6\u30ca\u3092\u53c2\u7167\u3057\u307e\u3059\u3002<\/p>\n\u30c7\u30fc\u30bf\u3092GridDB\u306b\u683c\u7d0d\u3059\u308b<\/h2>\n
\u6b21\u306eJava\u30b3\u30fc\u30c9\u306f\u3001”Customers.csv “\u30d5\u30a1\u30a4\u30eb\u304b\u3089\u30c7\u30fc\u30bf\u3092\u8aad\u307f\u53d6\u308a\u3001\u305d\u308c\u3092GridDB\u306b\u683c\u7d0d\u3059\u308b\u305f\u3081\u306b\u4f7f\u3044\u307e\u3059\u3002<\/p>\n
\n
File file1 = new File(\"Customers.csv\");\n Scanner sc = new Scanner(file1);\n String data = sc.next();\n \n while (sc.hasNext()){\n String scData = sc.next();\n String dataList[] = scData.split(\",\");\n String income = dataList[0];\n String spending_score = dataList[1];\n \n \n Customers customers = new Customers();\n \n customers.income = income;\n customers.spending_score = spending_score;\n coll.append(customers);\n }<\/code><\/pre>\n<\/div>\n\u3053\u306e\u30b3\u30fc\u30c9\u3067\u306f\u3001”Customers.csv “\u30d5\u30a1\u30a4\u30eb\u304b\u3089\u30c7\u30fc\u30bf\u3092\u8aad\u307f\u8fbc\u307f\u3001customers<\/code>\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u4f5c\u6210\u3057\u3066\u3044\u307e\u3059\u3002\u305d\u3057\u3066\u3001\u305d\u306e customers<\/code> \u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092 GridDB \u30b3\u30f3\u30c6\u30ca\u306b\u8ffd\u52a0\u3057\u3066\u3044\u307e\u3059\u3002.csv \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u533a\u5207\u308a\u6587\u5b57\u306b\u306f\u30ab\u30f3\u30de (,) \u3092\u4f7f\u7528\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\nGridDB\u304b\u3089\u30c7\u30fc\u30bf\u3092\u53d6\u5f97\u3059\u308b<\/h2>\n
\u3053\u308c\u3067\u3001GridDB\u304b\u3089\u30c7\u30fc\u30bf\u3092\u53d6\u308a\u51fa\u3057\u3001K-Means\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u4f7f\u3063\u3066\u5206\u6790\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u4ee5\u4e0b\u306e\u30b3\u30fc\u30c9\u306f\u3001GridDB\u306b\u30c7\u30fc\u30bf\u3092\u7167\u4f1a\u3059\u308b\u305f\u3081\u306e\u3082\u306e\u3067\u3059\u3002<\/p>\n
\n
Query<customers> query = coll.query(\"select *\");\n RowSet<\/customers><customers> rs = query.fetch(false);\n RowSet res = query.fetch();<\/customers><\/code><\/pre>\n<\/div>\nselect *<\/code>\u6587\u306f\u3001\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u30b3\u30f3\u30c6\u30ca\u304b\u3089\u3059\u3079\u3066\u306e\u30c7\u30fc\u30bf\u3092\u7167\u4f1a\u3059\u308b\u306e\u306b\u5f79\u7acb\u3061\u307e\u3059\u3002<\/p>\n\u30c7\u30fc\u30bf\u3092\u30af\u30e9\u30b9\u30bf\u30fc\u5316\u3059\u308b<\/h2>\n
\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u304b\u3089\u30c7\u30fc\u30bf\u3092\u53d6\u308a\u51fa\u3057\u305f\u306e\u3067\u3001K-Means\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u4f7f\u3063\u3066\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u3092\u884c\u3044\u307e\u3059\u3002\u30c7\u30fc\u30bf\u304b\u30892\u3064\u306e\u30af\u30e9\u30b9\u30bf\u3092\u4f5c\u6210\u3059\u308b\u306e\u3067\u3001k<\/code>\u306e\u5024\u30922\u306b\u8a2d\u5b9a\u3057\u307e\u3059\u3002<\/p>\n\u307e\u305f\u30012\u3064\u306e\u70b9\u3092\u521d\u671f\u5316\u3057\u3066\u30012\u3064\u306e\u30af\u30e9\u30b9\u30bf\u306e\u30bb\u30f3\u30c8\u30ed\u30a4\u30c9\u306e\u521d\u671f\u30bb\u30f3\u30c8\u30ed\u30a4\u30c9\u3068\u3057\u3066\u6a5f\u80fd\u3055\u305b\u307e\u3059\u3002\u3053\u306e\u30b3\u30fc\u30c9\u3067\u306f\u3001\u5404\u30c7\u30fc\u30bf\u30a2\u30a4\u30c6\u30e0\u3068\u30bb\u30f3\u30c8\u30ed\u30a4\u30c9\u306e\u9593\u306e\u8ddd\u96e2\u3092\u8a08\u7b97\u3057\u3001\u5404\u30c7\u30fc\u30bf\u30a2\u30a4\u30c6\u30e0\u3092\u6700\u3082\u8fd1\u3044\u30af\u30e9\u30b9\u30bf\u306b\u5272\u308a\u5f53\u3066\u307e\u3059\u3002<\/p>\n
\u3053\u306e\u30b3\u30fc\u30c9\u306f\u3001\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u30bb\u30f3\u30c8\u30ed\u30a4\u30c9\u304c\u5909\u5316\u3057\u306a\u304f\u306a\u308b\u3068\u3001\u53cd\u5fa9\u51e6\u7406\u3092\u505c\u6b62\u3057\u307e\u3059\u3002<\/p>\n
\u30b3\u30fc\u30c9\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\n
\n
int x,j,k=2;\n int cluster1[][] = new int[18][10];\n int cluster2[][] = new int[20][80];\n float mean1[][] = new float[1][2];\n float mean2[][] = new float[1][2];\n float temp1[][] = new float[1][2], temp2[][] = new float[1][2];\n int sum11 = 0, sum12 = 0, sum21 = 0, sum22 = 0;\n double dist1, dist2;\n int i1 = 0, i2 = 0, itr = 0;\n \n \n System.out.println(\"nNumber of clusters: \"+k);\n \n \/\/ Set random means\n mean1[0][0] = 18;\n mean1[0][1] = 10;\n mean2[0][0] = 20;\n mean2[0][1] = 80;\n \n \/\/ Loop untill the new mean and previous mean are the same\n while(!Arrays.deepEquals(mean1, temp1) || !Arrays.deepEquals(mean2, temp2)) {\n \n \/\/Empty the partitions\n for(x=0;x<10;x++) {\n cluster1[x][0] = 0;\n cluster1[x][1] = 0;\n cluster2[x][0] = 0;\n cluster2[x][1] = 0;\n }\n \n i1 = 0; i2 = 0;\n \n \/\/Find the distance between mean and the data point and store it in its corresponding partition\n for(x=0;x<10;x++) {\n dist1 = Math.sqrt(Math.pow(res[x][0] - mean1[0][0],2) + Math.pow(res[x][1] - mean1[0][1],2));\n dist2 = Math.sqrt(Math.pow(res[x][0] - mean2[0][0],2) + Math.pow(res[x][1] - mean2[0][1],2));\n \n if(dist1 < dist2) {\n cluster1[i1][0] = res[x][0];\n cluster1[i1][1] = res[x][1];\n \n i1++;\n }\n else {\n cluster2[i2][0] = res[x][0];\n cluster2[i2][1] = res[x][1];\n \n i2++;\n }\n }\n \n \/\/Store the previous mean\n temp1[0][0] = mean1[0][0];\n temp1[0][1] = mean1[0][1];\n temp2[0][0] = mean2[0][0];\n temp2[0][1] = mean2[0][1];\n \n \/\/Find the new mean for new partitions\n sum11 = 0; sum12 = 0; sum21 = 0; sum22 = 0;\n\n for(x=0;x<i1;x++) {\n sum11 += cluster1[x][0];\n sum12 += cluster1[x][1];\n }\n for(x=0;x<i2;x++) {\n sum21 += cluster2[x][0];\n sum22 += cluster2[x][1];\n }\n mean1[0][0] = (float)sum11\/i1;\n mean1[0][1] = (float)sum12\/i1;\n mean2[0][0] = (float)sum21\/i2;\n mean2[0][1] = (float)sum22\/i2;\n \n itr++;\n }\n \n System.out.println(\"Cluster1:\");\n for(x=0;x<i1;x++) {\n System.out.println(cluster1[x][0]+\" \"+cluster1[x][1]);\n }\n System.out.println(\"nCluster2:\");\n for(x=0;x<i2;x++) {\n System.out.println(cluster2[x][0]+\" \"+cluster2[x][1]);\n }\n System.out.println(\"nFinal Mean: \");\n System.out.println(\"Mean1 : \"+mean1[0][0]+\" \"+mean1[0][1]);\n System.out.println(\"Mean2 : \"+mean2[0][0]+\" \"+mean2[0][1]);\n System.out.println(\"nTotal Iterations: \"+itr);<\/code><\/pre>\n<\/div>\n\u30b3\u30fc\u30c9\u3092\u30b3\u30f3\u30d1\u30a4\u30eb\u3057\u5b9f\u884c\u3059\u308b<\/h2>\n
\u307e\u305a\u3001gsadm<\/code>\u30e6\u30fc\u30b6\u3067\u30ed\u30b0\u30a4\u30f3\u3057\u307e\u3059\u3002\u4f5c\u6210\u3057\u305f.java<\/code>\u30d5\u30a1\u30a4\u30eb\u3092\u3001\u4ee5\u4e0b\u306e\u30d1\u30b9\u306b\u3042\u308bGridDB\u306ebin<\/code>\u30d5\u30a9\u30eb\u30c0\u306b\u79fb\u52d5\u3057\u307e\u3059\u3002<\/p>\n\/griddb_4.6.0-1_amd64\/usr\/griddb-4.6.0\/bin<\/p>\n
\u6b21\u306b\u3001Linux\u306e\u30bf\u30fc\u30df\u30ca\u30eb\u3067\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3057\u3001gridstore.jar\u30d5\u30a1\u30a4\u30eb\u306e\u30d1\u30b9\u3092\u8a2d\u5b9a\u3057\u307e\u3059\u3002<\/p>\n
export CLASSPATH=$CLASSPATH:\/home\/osboxes\/Downloads\/griddb_4.6.0-1_amd64\/usr\/griddb-4.6.0\/bin\/gridstore.jar\n<\/code><\/pre>\n\u6b21\u306b\u3001\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3057\u3066\u3001.java<\/code>\u30d5\u30a1\u30a4\u30eb\u3092\u30b3\u30f3\u30d1\u30a4\u30eb\u3057\u307e\u3059\u3002<\/p>\njavac Kmeans.java\n<\/code><\/pre>\n\u4ee5\u4e0b\u306e\u30b3\u30de\u30f3\u30c9\u3092\u5b9f\u884c\u3057\u3066\u751f\u6210\u3055\u308c\u305f.class\u30d5\u30a1\u30a4\u30eb\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002<\/p>\n
java Kmeans\n<\/code><\/pre>\n\u3053\u306e\u30b3\u30fc\u30c9\u3067\u306f\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u30012\u3064\u306e\u30af\u30e9\u30b9\u30bf\u30fc\u305d\u308c\u305e\u308c\u306b\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u3055\u308c\u305f\u30c7\u30fc\u30bf\u30dd\u30a4\u30f3\u30c8\u3001\u5404\u30af\u30e9\u30b9\u30bf\u30fc\u306e\u5e73\u5747\u5024\u3001\u304a\u3088\u3073\u5b9f\u884c\u3055\u308c\u305f\u53cd\u5fa9\u56de\u6570\u304c\u8868\u793a\u3055\u308c\u307e\u3059\u3002\u540c\u3058\u30af\u30e9\u30b9\u30bf\u30fc\u306b\u5165\u308c\u3089\u308c\u305f\u30c7\u30fc\u30bf\u30dd\u30a4\u30f3\u30c8\u306f\u3001\u4ed6\u306e\u30af\u30e9\u30b9\u30bf\u30fc\u306b\u5165\u308c\u3089\u308c\u305f\u30c7\u30fc\u30bf\u30dd\u30a4\u30f3\u30c8\u3088\u308a\u3082\u4e92\u3044\u306b\u4f3c\u3066\u3044\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/p>\n
Number of clusters: 2\nCluster1:\n15 39\n16 6\n17 40\n18 6\n19 3\n\nCluster2:\n15 89\n16 77\n17 76\n18 94\n19 72\n\nFinal Mean: \nMean1 : 17.0 18.8\nMean2 : 17.0 81.6\n\nTotal Iterations: 2\n<\/code><\/pre>\n\u3053\u308c\u3067\u5b8c\u4e86\u3067\u3059\u3002\u4ee5\u4e0a\u3001Java\u3068GridDB\u3092\u4f7f\u3063\u305fK-Means\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u5b9f\u88c5\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3057\u305f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
\u306f\u3058\u3081\u306b \u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306f\u30c7\u30fc\u30bf\u30de\u30a4\u30cb\u30f3\u30b0\u306e\u4ee3\u8868\u7684\u306a\u30bf\u30b9\u30af\u3067\u3059\u3002\u3053\u306e\u30bf\u30b9\u30af\u3067\u306f\u3001\u30c7\u30fc\u30bf\u30a2\u30a4\u30c6\u30e0\u3092\u3044\u304f\u3064\u304b\u306e\u610f\u5473\u306e\u3042\u308b\u30b0\u30eb\u30fc\u30d7\u307e\u305f\u306f\u30af\u30e9\u30b9\u30bf\u306b\u5206\u5272\u3057\u307e\u3059\u3002\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306f\u3001\u30c7\u30fc\u30bf\u30a2\u30a4\u30c6\u30e0\u9593\u306e\u57fa\u672c\u7684\u306a\u95a2\u4fc2\u3092\u7279\u5b9a\u3059\u308b\u306e\u306b\u5f79\u7acb\u3061 […]<\/p>\n","protected":false},"author":41,"featured_media":50232,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[1005],"tags":[],"class_list":["post-50762","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-1005"],"acf":[],"yoast_head":"\n
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