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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸°úÇÐȸ ³í¹®Áö > Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)

Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)

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ÇѱÛÁ¦¸ñ(Korean Title) Min-Hash¸¦ ÀÌ¿ëÇÑ È¿À²ÀûÀÎ ´ë¿ë·® ±×·¡ÇÁ Ŭ·¯½ºÅ͸µ ±â¹ý
¿µ¹®Á¦¸ñ(English Title) An Efficient Large Graph Clustering Technique based on Min-Hash
ÀúÀÚ(Author) À̼®ÁÖ   ¹ÎÁر⠠ Seok-Joo Lee   Jun-Ki Min  
¿ø¹®¼ö·Ïó(Citation) VOL 43 NO. 03 PP. 0380 ~ 0388 (2016. 03)
Çѱ۳»¿ë
(Korean Abstract)
±×·¡ÇÁ Ŭ·¯½ºÅ͸µÀº ¼­·Î À¯»çÇÑ Æ¯¼ºÀ» °®´Â Á¤Á¡µéÀ» µ¿ÀÏÇÑ Å¬·¯½ºÅÍ·Î ¹­´Â ±â¹ýÀ¸·Î ±×·¡ÇÁ µ¥ÀÌÅ͸¦ ºÐ¼®ÇÏ°í ±× Æ¯¼ºÀ» ÆľÇÇϴµ¥ Æø³Ð°Ô »ç¿ëµÈ´Ù. ÃÖ±Ù ¼Ò¼È ³×Æ®¿öÅ© ¼­ºñ½º¿Í ¿ùµå ¿ÍÀ̵å À¥, ÅÚ·¹Æù ³×Æ®¿öÅ© µîÀÇ ´Ù¾çÇÑ ÀÀ¿ëºÐ¾ß¿¡¼­ Å©±â°¡ Å« ´ë¿ë·® ±×·¡ÇÁ µ¥ÀÌÅÍ°¡ »ý¼ºµÇ°í ÀÖ´Ù. ÀÌ¿¡ µû¶ó¼­ ´ë¿ë·® ±×·¡ÇÁ µ¥ÀÌÅ͸¦ È¿À²ÀûÀ¸·Î ó¸®Çϴ Ŭ·¯½ºÅ͸µ ±â¹ýÀÇ Á߿伺ÀÌ Áõ°¡ÇÏ°í ÀÖ´Ù. º» ³í¹®¿¡¼­´Â ´ë¿ë·® ±×·¡ÇÁ µ¥ÀÌÅÍÀÇ Å¬·¯½ºÅ͵éÀ» È¿À²ÀûÀ¸·Î »ý¼ºÇϴ Ŭ·¯½ºÅ͸µ ¾Ë°í¸®ÁòÀ» Á¦¾ÈÇÑ´Ù. ¿ì¸®ÀÇ Á¦¾È ±â¹ýÀº ±×·¡ÇÁ ³»ÀÇ Å¬·¯½ºÅÍµé °£ÀÇ À¯»çµµ¸¦ Min-Hash¸¦ ÀÌ¿ëÇÏ¿© È¿°úÀûÀ¸·Î ÃßÁ¤ÇÏ°í °è»êµÈ À¯»çµµ¿¡ µû¶ó¼­ Ŭ·¯½ºÅ͵éÀ» »ý¼ºÇÑ´Ù. ½Ç¼¼°è µ¥ÀÌÅ͸¦ ÀÌ¿ëÇÑ ½ÇÇè¿¡¼­ ¿ì¸®´Â º» ³í¹®¿¡¼­ Á¦¾ÈÇÏ´Â ±â¹ý°ú ±âÁ¸ ±×·¡ÇÁ Ŭ·¯½ºÅ͸µ ±â¹ýµé°ú ºñ±³ÇÏ¿© Á¦¾È±â¹ýÀÇ È¿À²¼ºÀ» º¸¿´´Ù.
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(English Abstract)
Graph clustering is widely used to analyze a graph and identify the properties of a graph by generating clusters consisting of similar vertices. Recently, large graph data is generated in diverse applications such as Social Network Services (SNS), the World Wide Web (WWW), and telephone networks. Therefore, the importance of graph clustering algorithms that process large graph data efficiently becomes increased. In this paper, we propose an effective clustering algorithm which generates clusters for large graph data efficiently. Our proposed algorithm effectively estimates similarities between clusters in graph data using Min-Hash and constructs clusters according to the computed similarities. In our experiment with real-world data sets, we demonstrate the efficiency of our proposed algorithm by comparing with existing algorithms.
Å°¿öµå(Keyword) ±×·¡ÇÁ Ŭ·¯½ºÅ͸µ   ¹ÎÇؽà  µ¥ÀÌÅÍ ¸¶ÀÌ´×   ´ë¿ë·® ±×·¡ÇÁ   graph clustering   min-hash   data mining   large graph  
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