[B! clustering] chezouã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯

chezou id:chezou

clusteringã«é–¢ã™ã‚‹chezouã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (5)

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ã¤ã‚Œã¥ã‚Œæ—¥è¨˜: pottsãƒ¢ãƒ‡ãƒ«ã®ã‚¯ãƒ©ã‚¹ã‚¿ãƒªãƒ³ã‚°ã¸ã®å¿œç”¨
2011å¹´11æœˆ30æ—¥æ°´æ›œæ—¥ pottsãƒ¢ãƒ‡ãƒ«ã®ã‚¯ãƒ©ã‚¹ã‚¿ãƒªãƒ³ã‚°ã¸ã®å¿œç”¨ potts modelã¨ã¯ã€ã‚’è¿°ã¹ã‚‹å‰ã«ç°¡å˜ãªä¾‹ã‚’è€ƒãˆã‚‹ã€‚ä»Šç›®ã®å‰ã«Næ¥µã¨Sæ¥µã®ç£çŸ³ã‚’åŒæ™‚ã«ã°ã‚‰ã¾ã„ãŸã‚‰ã€ã©ã®ã‚ˆã†ãªçŠ¶æ…‹ã«ãªã‚‹ã ã‚ã†ã‹ï¼Ÿ å…·ä½“çš„ã«ã©ã®ã‚ˆã†ãªçŠ¶æ…‹ã«ãªã‚‹ã®ã‹ã«ã¤ã„ã¦ã¯è¤‡é›‘ã™ãŽã¦æƒ³åƒã‚‚ã§ããªã„ãŒã€å°‘ãªãã¨ã‚‚ã€Œã©ã®ã‚ˆã†ãªåŽŸç†ã€ã«å¾“ã£ã¦ã€çµæžœã¨ã—ã¦ã®çŠ¶æ…‹ã«ãªã‚‹ã®ã‹ã¯ç‰©ç†å¦çš„ãªè¦³å¯Ÿã«ã‚ˆã£ã¦ã‚ã‚‹ç¨‹åº¦ç†è§£ã§ãã‚‹ã ã‚ã†ã€‚ ã¾ãšä¸€ã¤ã¯ã€Œã‚¨ãƒãƒ«ã‚®ãƒ¼ã€ã§ã‚ã‚‹ã€‚ç‰©ç†æ³•å‰‡ã§ã¯ã€Œç£å ´ã€ã«ã¤ã„ã¦ã€Œç£å ´ã‚¨ãƒãƒ«ã‚®ãƒ¼ã€ãŒå®šç¾©ã•ã‚Œã‚‹ã€‚åŒã˜æ¥µåŒå£«ã¯åç™ºã™ã‚‹ã“ã¨ã¯ç§ãŸã¡ã®çµŒé¨“å‰‡ã‹ã‚‰ã‚ã‹ã‚‹ã€‚åç™ºã™ã‚‹ã®ã¯ã€ç£çŸ³ã®é–“ã«ã€Œã‚¨ãƒãƒ«ã‚®ãƒ¼ã€ãŒå˜åœ¨ã—ã¦ãã‚ŒãŒã€ŒåŠ›ã€ã¨ã—ã¦è¡¨ã‚Œã¦ã„ã‚‹ã¨è§£é‡ˆã§ãã‚‹ã€‚ ãã—ã¦äºŒã¤ç›®ã¯ã€Œã‚¨ãƒ³ãƒˆãƒãƒ”ãƒ¼ã®æ³•å‰‡ã€ã§ã‚ã‚‹ã€‚ã‚¨ãƒ³ãƒˆãƒãƒ”ãƒ¼ã¨ã¯ãŠãŠã–ã£ã±ã«è¨€ãˆã°ã€ã€Œæ•£ã‚‰ã°ã‚Šå…·åˆã€ã¨ã‹ã€Œä¹±é›‘ã•ã€ã¨ã„ã†è¨€è‘‰ã«ã‚ˆã£ã¦ã‚‚è¡¨ç¾ã•ã‚Œã‚‹ã€‚è‡ªç„¶ç•Œã®ç¾è±¡ã«ãŠã„ã¦
chezou 2014/05/08
clustering
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ã‚¯ãƒ©ã‚¹ã‚¿ãƒªãƒ³ã‚°çµæžœã®è©•ä¾¡ã®å°ºåº¦åŸºæº– - froglog
ã“ã®ã‚¨ãƒ³ãƒˆãƒªã«ã¤ã„ã¦ ã‚¯ãƒ©ã‚¹ã‚¿ãƒªãƒ³ã‚°ã®çµæžœã‚’å®šé‡è©•ä¾¡ã™ã‚‹ã¨ãã®åŸºæº–ã‚’æ•°å¹´ã«1å›žãã‚‰ã„èª¿ã¹ã¦ã‚‹æ°—ãŒã™ã‚‹ã®ã¨ã€æ—¥æœ¬èªžã§ã‚ã¾ã‚Šã¾ã¨ã¾ã£ãŸæƒ…å ±ã‚’è¦‹ãªã„æ°—ãŒã—ãŸã®ã§æŒ™ã’ã¦ã¿ã¾ã™ã€‚ä»Šå›žæŒ™ã’ã‚‹ã®ã¯ãƒãƒ¼ãƒ‰ï¼ˆã‚¯ãƒªã‚¹ãƒ—ï¼‰ã‚¯ãƒ©ã‚¹ã‚¿ãƒªãƒ³ã‚°ã«ã¤ã„ã¦ã®æŒ‡æ¨™ã§ã™ã€‚å¾Œã§è¿½åŠ ã™ã‚‹ã‹ã‚‚ã€‚ ã‚¯ãƒ©ã‚¹ã‚¿å†…è·é›¢äºŒä¹—å’Œ ã¨ã„ã†å‘¼ã³æ–¹ãŒæ£ã—ã„ã®ã‹ã©ã†ã‹ã‚ã‹ã‚Šã¾ã›ã‚“ãŒã€k-means æ³•ã®å ´åˆã¯ã“ã®å€¤ã‚’ç¹°ã‚Šè¿”ã—å‡¦ç†ã®çµæžœã€æ¥µå°åŒ–ã™ã‚‹ã‚ˆã†ã«ãªã£ã¦ãŠã‚Š*1ã€é‡è¦ãªæŒ‡æ¨™ã¨ãªã‚Šã¾ã™ã€‚ ã‚¯ãƒ©ã‚¹ã‚¿å†…ã®å‡é›†æ€§ã‚’è¡¨ç¾ã—ã¾ã™ã€‚ $ P_k = \sum_{i=1}^k \sum_{x \in C_{i} } \left( d(x, c_i) \right) ^2 $ $ k $: ã‚¯ãƒ©ã‚¹ã‚¿æ•° $ C_i $: iç•ªç›®ã®ã‚¯ãƒ©ã‚¹ã‚¿ $ x $: ã‚¯ãƒ©ã‚¹ã‚¿ã®ãƒ¡ãƒ³ãƒãƒ¼ $ c_i $: iç•ªç›®ã®ã‚¯ãƒ©ã‚¹ã‚¿ã®ã‚»ãƒ³ãƒˆãƒã‚¤ãƒ‰ Pseudo F Cali
chezou 2014/05/06
clustering
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ã‚¯ãƒ©ã‚¹ã‚¿ãƒªãƒ³ã‚° (ã‚¯ãƒ©ã‚¹ã‚¿ãƒ¼åˆ†æž) - Toshihiro Kamishima
ã‚¯ãƒ©ã‚¹ã‚¿ãƒªãƒ³ã‚° (clustering) ã¨ã¯ï¼Œåˆ†é¡žå¯¾è±¡ã®é›†åˆã‚’ï¼Œå†…çš„çµåˆ (internal cohesion) ã¨å¤–çš„åˆ†é›¢ (external isolation) ãŒé”æˆã•ã‚Œã‚‹ã‚ˆã†ãªéƒ¨åˆ†é›†åˆã«åˆ†å‰²ã™ã‚‹ã“ã¨ [Everitt 93, å¤§æ©‹ 85] ã§ã™ï¼Žçµ±è¨ˆè§£æžã‚„å¤šå¤‰é‡è§£æžã®åˆ†é‡Žã§ã¯ã‚¯ãƒ©ã‚¹ã‚¿ãƒ¼åˆ†æž (cluster analysis) ã¨ã‚‚å‘¼ã°ã‚Œï¼ŒåŸºæœ¬çš„ãªãƒ‡ãƒ¼ã‚¿è§£æžæ‰‹æ³•ã¨ã—ã¦ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°ã§ã‚‚é »ç¹ã«åˆ©ç”¨ã•ã‚Œã¦ã„ã¾ã™ï¼Ž åˆ†å‰²å¾Œã®å„éƒ¨åˆ†é›†åˆã¯ã‚¯ãƒ©ã‚¹ã‚¿ã¨å‘¼ã°ã‚Œã¾ã™ï¼Žåˆ†å‰²ã®æ–¹æ³•ã«ã‚‚å¹¾ã¤ã‹ã®ç¨®é¡žãŒã‚ã‚Šï¼Œå…¨ã¦ã®åˆ†é¡žå¯¾è±¡ãŒã¡ã‚‡ã†ã©ä¸€ã¤ã ã‘ã®ã‚¯ãƒ©ã‚¹ã‚¿ã®è¦ç´ ã¨ãªã‚‹å ´åˆ(ãƒãƒ¼ãƒ‰ãªã‚‚ã—ãã¯ï¼Œã‚¯ãƒªã‚¹ãƒ—ãªã‚¯ãƒ©ã‚¹ã‚¿ã¨ã„ã„ã¾ã™)ã‚„ï¼Œé€†ã«ä¸€ã¤ã®ã‚¯ãƒ©ã‚¹ã‚¿ãŒè¤‡æ•°ã®ã‚¯ãƒ©ã‚¹ã‚¿ã«åŒæ™‚ã«éƒ¨åˆ†çš„ã«æ‰€å±žã™ã‚‹å ´åˆ(ã‚½ãƒ•ãƒˆï¼Œã¾ãŸã¯ï¼Œãƒ•ã‚¡ã‚¸ã‚£ãªã‚¯ãƒ©ã‚¹ã‚¿ã¨ã„ã„ã¾ã™)ãŒã‚ã‚Šã¾ã™ï¼Žã“ã“ã§ã¯å‰è€…ã®ãƒãƒ¼ãƒ‰ãªå ´åˆã®ã‚¯ãƒ©ã‚¹ã‚¿ãƒªãƒ³ã‚°ã«ã¤ã„ã¦è¿°ã¹ã¾ã™ï¼Ž
chezou 2013/05/23
ãªã‚“ã¨ã‚ã‹ã‚Šã‚„ã™ã„ã¾ã¨ã‚ï¼

clustering
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Pfi last seminar
1. ã“ã‚“ãªæˆæžœç™ºè¡¨ã§å¤§ä¸ˆå¤«ã‹ï¼Ÿ å¤§ä¸ˆå¤«ã ã€å•é¡Œãªã„ã€‚ PFI Seminar ã‚¤ãƒ³ã‚¿ãƒ¼ãƒ³æˆæžœç™ºè¡¨ å¤§å²©ç§€å’Œ @kisa12012 1 2. è‡ªå·±ç´¹ä»‹ ï½ å¤§å²©ç§€å’Œ ï½ M1@æ±äº¬å¤§å¦ ï½ å°‚é–€ï¼šæ©Ÿæ¢°å¦ç¿’ã€è‡ªç„¶è¨€èªžå‡¦ç† ï½ å¦éƒ¨ã§ã¯ã€é‡‘èžçµ±è¨ˆã®åˆ†æž ï½ Twitter : @kisa12012 ï½ ã¯ã¦ãª : id:kisa12012 ï½ http://d.hatena.ne.jp/kisa12012/ ï½ ãƒ¡ãƒ³ã‚¿ãƒ¼ ï½ @tkng ï½ @jnishi 2 3. ç›®æ¬¡ ï½ ã‚¯ãƒ©ã‚¹ã‚¿ãƒªãƒ³ã‚°ãƒ©ã‚¤ãƒ–ãƒ©ãƒª â€“Coalaâ€“ ï½ KMeans++/XMeans/Nearest Neightbor Clustering ï½ ç²¾åº¦/é€Ÿåº¦è©•ä¾¡å®Ÿé¨“ ï½ XMeansã«ã‚ˆã‚‹ã‚¯ãƒ©ã‚¹ã‚¿ã®éšŽå±¤åŒ– ï½ Sedueã‚¯ãƒ©ã‚¹ã‚¿ãƒªãƒ³ã‚°ãƒ‡ãƒ¢ ï½ è·é›¢è¨ˆç®—ãƒ©ã‚¤ãƒ–ãƒ©ãƒªãƒ‡ãƒ¢ ï½ è¨˜äº‹ã‚«ãƒ†ã‚´ãƒªæŠ½å‡º ï½ ã‚¨ãƒ³ãƒˆãƒãƒ”ãƒ¼ãƒ™ãƒ¼ã‚¹ã«ã‚ˆã‚‹ã‚«ãƒ†ã‚´ãƒªå˜
chezou 2013/04/18
K-means++,X-meansãªã©

clustering
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ã€ã‚¤ãƒ³ãƒ•ã‚©ã‚·ãƒ¼ã‚¯ã€‘Infoseek ï¼š æ¥½å¤©ãŒé‹å–¶ã™ã‚‹ãƒãƒ¼ã‚¿ãƒ«ã‚µã‚¤ãƒˆ
æ—¥é ƒã‚ˆã‚Šæ¥½å¤©ã®ã‚µãƒ¼ãƒ“ã‚¹ã‚’ã”åˆ©ç”¨ã„ãŸã ãã¾ã—ã¦ã‚ã‚ŠãŒã¨ã†ã”ã–ã„ã¾ã™ã€‚ ã‚µãƒ¼ãƒ“ã‚¹ã‚’ã”åˆ©ç”¨ã„ãŸã ã„ã¦ãŠã‚Šã¾ã™ã¨ã“ã‚å¤§å¤‰ç”³ã—è¨³ã”ã–ã„ã¾ã›ã‚“ãŒã€ç¾åœ¨ã€ç·Šæ€¥ãƒ¡ãƒ³ãƒ†ãƒŠãƒ³ã‚¹ã‚’è¡Œã‚ã›ã¦ã„ãŸã ã„ã¦ãŠã‚Šã¾ã™ã€‚ ãŠå®¢æ§˜ã«ã¯ã€ç·Šæ€¥ã®ãƒ¡ãƒ³ãƒ†ãƒŠãƒ³ã‚¹ã«ã‚ˆã‚Šã€ã”è¿·æƒ‘ã‚’ãŠã‹ã‘ã—ã¦ãŠã‚Šã€èª ã«ç”³ã—è¨³ã”ã–ã„ã¾ã›ã‚“ã€‚ ãƒ¡ãƒ³ãƒ†ãƒŠãƒ³ã‚¹ãŒçµ‚äº†æ¬¡ç¬¬ã€ã‚µãƒ¼ãƒ“ã‚¹ã‚’å¾©æ—§ã„ãŸã—ã¾ã™ã®ã§ã€ ä»Šã—ã°ã‚‰ããŠå¾…ã¡ã„ãŸã ã‘ã¾ã™ã‚ˆã†ã€ãŠé¡˜ã„ç”³ã—ä¸Šã’ã¾ã™ã€‚
chezou 2008/01/08
k-meansæ³•ãªã©ã®ã‚¯ãƒ©ã‚¹ã‚¿ãƒªãƒ³ã‚°

clustering

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