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ÇѱÛÁ¦¸ñ(Korean Title) |
K-Means Ŭ·¯½ºÅ͸µ ¼º´É Çâ»óÀ» À§ÇÑ ÃÖ´ëÆò±Õ°Å¸® ±â¹Ý ÃʱⰪ ¼³Á¤ |
¿µ¹®Á¦¸ñ(English Title) |
Refining Initial Seeds using Max Average Distance for K-Means Clustering |
ÀúÀÚ(Author) |
À̽ſø
ÀÌ¿øÈÖ
Shin-Won Lee
Won-Hee Lee
|
¿ø¹®¼ö·Ïó(Citation) |
VOL 12 NO. 02 PP. 0103 ~ 0111 (2011. 04) |
Çѱ۳»¿ë (Korean Abstract) |
´ë±Ô¸ð µ¥ÀÌÅÍ¿¡ ´ëÇÑ Æ¯¼º¿¡ µû¶ó ¸î °³ÀÇ Å¬·¯½ºÅÍ·Î ±ºÁýÈÇϴ Ŭ·¯½ºÅ͸µ ±â¹ýÀº °èÃþÀû Ŭ·¯½ºÅ͸µÀ̳ª ºÐÇÒ Å¬·¯½ºÅ͸µ µî ´Ù¾çÇÑ ±â¹ýÀÌ Àִµ¥ ±× Áß¿¡¼ K-Means ¾Ë°í¸®ÁòÀº ±¸ÇöÀÌ ½¬¿ì³ª ÇÒ´ç-Àç°è»ê¿¡ ¼Ò¿äµÇ´Â ½Ã°£ÀÌ Áõ°¡ÇÏ°Ô µÈ´Ù. º» ³í¹®¿¡¼´Â Ãʱâ Ŭ·¯½ºÅÍ Áß½Éµé °£ÀÇ °Å¸®°¡ ÃÖ´ë°¡ µÇµµ·Ï ÇÏ¿© Ãʱâ Ŭ·¯½ºÅÍ Á߽ɵéÀÌ °í¸£°Ô ºÐÆ÷µÇµµ·Ï ÇÔÀ¸·Î½á ÇÒ´ç-Àç°è»ê Ƚ¼ö¸¦ ÁÙÀÌ°í Àüü Ŭ·¯½ºÅ͸µ ½Ã°£À» °¨¼Ò½ÃÅ°°íÀÚ ÇÑ´Ù. |
¿µ¹®³»¿ë (English Abstract) |
Clustering methods is divided into hierarchical clustering, partitioning clustering, and more. If the amount of documents is huge, it takes too much time to cluster them in hierarchical clustering. In this paper we deal with K-Means algorithm that is one of partitioning clustering and is adequate to cluster so many documents rapidly and easily . We propose the new method of selecting initial seeds in K-Means algorithm. In this method, the initial seeds have been selected that are positioned as far away from each other as possible. |
Å°¿öµå(Keyword) |
Ŭ·¯½ºÅ͸µ
ÃʱⰪ
clustering
initial seed
K-Means
|
ÆÄÀÏ÷ºÎ |
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