{"id":27159,"date":"2026-01-02T09:00:40","date_gmt":"2026-01-02T00:00:40","guid":{"rendered":"https:\/\/blog.agentsoft.co.kr\/index.php\/2026\/01\/02\/27159\/"},"modified":"2026-01-02T09:00:40","modified_gmt":"2026-01-02T00:00:40","slug":"data-similarity-and-distance%eb%a5%bc-%ec%b8%a1%ec%a0%95%ed%95%98%eb%8a%94-measure","status":"publish","type":"post","link":"https:\/\/blog.agentsoft.co.kr\/index.php\/2026\/01\/02\/27159\/","title":{"rendered":"Data Similarity and Distance\ub97c \uce21\uc815\ud558\ub294 measure"},"content":{"rendered":"

<\/p>\n

\ubaa9\ucc28<\/strong><\/p>\n

1. Text Similarity Measures
\n2. Temporal Similarity Measures
\n3. Graph Similarity Measures
\n4. Supervised Similarity Functions<\/p>\n

\ubcf8\ubb38\ub0b4\uc6a9<\/strong><\/p>\n

– Similarity between Two Graphs
\n\uadf8\ub798\ud504\uac04 \uc720\uc0ac\ub3c4 \uacc4\uc0b0\uc5d0\uc11c \uc5b4\ub824\uc6b4 \uc694\uc778\uc740 \ub9ce\uc740 node\ub4e4\uc774 \uac19\uc740 \ub808\uc774\ube14\uc744 \uac00\uc9c0\uace0 \uc788\ub294 \uac83\uc774\ub2e4. \uc774 \ubb38 \uc81c\ub294 \ud558\ub098\uc758 \ubd84\uc790\uc5d0 \uac19\uc740 \uc6d0\uc18c\uac00 \uc874\uc7ac\ud558\ub294 \ud654\ud559\ubd84\uc57c \uac19\uc740 \uacf3\uc5d0\uc11c \ubc1c\uc0dd\ud55c\ub2e4. \uc774\uac83\uc744 NP-hard\ub77c\uace0 \uc54c \ub824\uc9c4 graph isomorphim problem(\ub3d9\ud615\uc774\uc9c8: \ubaa8\uc591\uc740 \uac19\uc740\ub370 \uc131\uc9c8\uc740 \ub2e4\ub978 \uac83)\uc774\ub77c\uace0 \ubd80\ub978\ub2e4. \ub530\ub77c \uc11c \ub2e4\uc591\ud55c \uc54c\uace0\ub9ac\uc998\ub4e4\uc774 \uc81c\uc548\ub418\uc5c8\ub2e4.<\/p>\n

-Maximum common subgraph distance: \ub450 \uadf8\ub798\ud504\uac00 \ud070 subgraph\ub97c \uac00\uc9c8 \ub54c \uc720\uc0ac\ud558\ub2e4\uace0 \uace0\ub824\ud55c\ub2e4.
\n-Substructure-based similarity: \uacf5\ud1b5\uc758 substructures\uc758 \uc218\ub97c \uc138\uc5b4\uc11c \uc720\uc0ac\ub3c4\ub97c \uce21\uc815\ud55c\ub2e4.
\n-Graph-edit distance: string-edit distance\uc640 \ube44\uc2b7\ud55c \ubc29\uc2dd\uc73c\ub85c \ud558\ub098\uc758 \uadf8\ub798\ud504\ub97c \ubaa9\ud45c\ub85c \ud558\ub294 \uadf8\ub798\ud504\uc5d0 \uc77c\uce58\uc2dc\ud0a4\uae30 \uc704\ud574\uc11c \uc5bc\ub9c8\ub098 \ub9ce\uc774 edit\uc744 \ud574\uc57c\ud558\ub294\uc9c0\uc5d0 \ub530\ub77c\uc11c \uc720\uc0ac\ub3c4\uac00 \uacb0\uc815\ub41c \ub2e4. \ud558\uc9c0\ub9cc \uaddc\ubaa8\uac00 \ud070 \uadf8\ub798\ud504\uc5d0\uc11c\ub294 \uc801\uc6a9\uc2dc\ud0a4\uae30\uac00 \uc5b4\ub835\ub2e4.
\n-Graph kernels: \ube44\uc2b7\ud55c\uac74 \ube44\uc2b7\ud558\uac8c, \ub2e4\ub978 \uac83\uc740 \ub354 \ub2e4\ub974\uac8c \ub9cc\ub4dc\ub294 kernel function\uc744 \ud1b5\ud574\uc11c \uc720\uc0ac\ub3c4\ub97c \uce21\uc815\ud55c\ub2e4.<\/p>\n

4. Supervised Similarity Functions
\n\ubd84\ub958\ubb38\uc81c\uc5d0 \uc801\uc6a9\ud558\ub294 function\uc73c\ub85c \ub192\uc740 value\ub97c \uac00\uc9c8\uc218\ub85d \ube44\uc720\uc0ac\uc131\uc774 \ucee4\uc9c0\ubbc0\ub85c distance function \uc774\ub2e4. \ud2b9\uc9d5\uc740 \uc774\uc804\uc758 measure\ub4e4\uc740 \uc0ac\uc6a9\uc790\uc758 \ub3c4\uc6c0\uc774 \uc5c6\uc774 \uc720\uc0ac\ub3c4\ub97c \uce21\uc815\ud588\uc9c0\ub9cc, \uc774\uac83\uc740 \ud2b9\uc131\uc758 \uad00\ub828 \uc131 \ub610\ub294 distance function\uc744 \uacb0\uc815\ud560 \ub54c \ub3c4\uba54\uc778 \uc9c0\uc2dd\uc5d0 \ud06c\uac8c \uc758\uc874\ud55c\ub2e4. \ub3c4\uba54\uc778 \uc9c0\uc2dd\uc5d0 \ub530\ub978 feedback\uc740 \uc544\ub798\uc640 \uac19\uc774 \ud45c\ud604\ub41c\ub2e4.<\/p>\n

\uac01 feature\ub9c8\ub2e4 feedback\uc744 \ubc14\ud0d5\uc73c\ub85c \uacb0\uc815\ub41c\uac00\uc911\uce58..<\/p>\n

<\uc911 \ub7b5><\/p>\n

\ucd9c\ucc98 : \ud574\ud53c\ucea0\ud37c\uc2a4<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

\ubaa9\ucc28 1. Text Similarity Measures 2. Temporal Similarity Measures 3. Graph Similarity Measures 4. Supervised Similarity Functions \ubcf8\ubb38\ub0b4\uc6a9 – Similarity between Two Graphs \uadf8\ub798\ud504\uac04 \uc720\uc0ac\ub3c4 \uacc4\uc0b0\uc5d0\uc11c \uc5b4\ub824\uc6b4 \uc694\uc778\uc740 \ub9ce\uc740 node\ub4e4\uc774 \uac19\uc740 \ub808\uc774\ube14\uc744 \uac00\uc9c0\uace0 \uc788\ub294 \uac83\uc774\ub2e4. \uc774 \ubb38 \uc81c\ub294 \ud558\ub098\uc758 \ubd84\uc790\uc5d0 \uac19\uc740 \uc6d0\uc18c\uac00 \uc874\uc7ac\ud558\ub294 \ud654\ud559\ubd84\uc57c \uac19\uc740 \uacf3\uc5d0\uc11c \ubc1c\uc0dd\ud55c\ub2e4. \uc774\uac83\uc744 NP-hard\ub77c\uace0 \uc54c \ub824\uc9c4 graph isomorphim problem(\ub3d9\ud615\uc774\uc9c8: \ubaa8\uc591\uc740 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[20803,4108,33908,9992,12593],"class_list":["post-27159","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-measurement","tag-power-distance","tag-similarity","tag-9992","tag-12593"],"_links":{"self":[{"href":"https:\/\/blog.agentsoft.co.kr\/index.php\/wp-json\/wp\/v2\/posts\/27159","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.agentsoft.co.kr\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.agentsoft.co.kr\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.agentsoft.co.kr\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.agentsoft.co.kr\/index.php\/wp-json\/wp\/v2\/comments?post=27159"}],"version-history":[{"count":0,"href":"https:\/\/blog.agentsoft.co.kr\/index.php\/wp-json\/wp\/v2\/posts\/27159\/revisions"}],"wp:attachment":[{"href":"https:\/\/blog.agentsoft.co.kr\/index.php\/wp-json\/wp\/v2\/media?parent=27159"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.agentsoft.co.kr\/index.php\/wp-json\/wp\/v2\/categories?post=27159"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.agentsoft.co.kr\/index.php\/wp-json\/wp\/v2\/tags?post=27159"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}