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µ¥ÀÌÅͺ£À̽º ¿¬±¸È¸Áö(SIGDB)
Current Result Document :
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´ÙÀ½°Ç
ÇѱÛÁ¦¸ñ(Korean Title)
½Ã°£°£°Ý À¯Æ¿¸®Æ¼¿¡ ±â¹ÝÇÑ È¿°úÀûÀÎ ¼øÂ÷ÆÐÅÏ ¸¶ÀÌ´× ¾Ë°í¸®Áò
¿µ¹®Á¦¸ñ(English Title)
Effective Sequential Pattern Mining Algorithm based on the Time-Interval Utility
ÀúÀÚ(Author)
ÀÌ°æÈÆ
ÃÖ¿ì½Ä
À̹ÎÀç
À̼®·æ
Kyung-Hun Lee
Woo-Sik Choi
Min-Jae Lee
Seok-Lyong Lee
¿ø¹®¼ö·Ïó(Citation)
VOL 31 NO. 01 PP. 0015 ~ 0028 (2015. 04)
Çѱ۳»¿ë
(Korean Abstract)
¾ÆÀÌÅÛÀÇ ÃâÇöºóµµ¿Í ¼ö·®¿¡ ±âÃÊÇÑ ¼øÂ÷ÆÐÅÏ ¸¶À̴׿¡ ´ëÇÑ ¿¬±¸°¡ È°¹ßÈ÷ ÀÌ·ç¾îÁ® ¿ÔÀ¸¸ç, ÃÖ±Ù µé¾î ¾ÆÀÌÅÛÀÇ °íÀ¯ÇÑ °¡Ä¡(profit)¸¦ °í·ÁÇÑ À¯Æ¿¸®Æ¼(utility) ÁöÇ¥¸¦ ôµµ·Î »ç¿ëÇÏ´Â À¯Æ¿¸®Æ¼ÇÑ ¼øÂ÷ÆÐÅÏ ±â¹ýÀÌ È°¹ßÈ÷ ¿¬±¸µÇ°í ÀÖ´Ù. ±×·¯³ª À¯Æ¿¸®Æ¼ ÁöÇ¥´Â ¾ÆÀÌÅÛµéÀÌ ¹ß»ýÇÏ´Â ½Ã°£°£°Ý¿¡ ´ëÇؼ´Â °í·ÁÇÏÁö ¾Ê±â ¶§¹®¿¡ ¼·Î ´Ù¸¥ ½Ã°£°£°ÝÀ» µÎ°í ¹ß»ýÇÏ´Â ´Ù¾çÇÑ Çö½Ç »óȲÀ» ¹Ý¿µÇϱ⠾î·Æ´Ù. º» ³í¹®¿¡¼´Â ÀÌ·¯ÇÑ ¹®Á¦Á¡¿¡ Âø¾ÈÇÏ¿© ½Ã°£°£°ÝÀ» ¹Ý¿µÇÑ »õ·Î¿î ÁöÇ¥¸¦ Á¦½ÃÇÑ´Ù. Á¦¾ÈÇÑ ¾Ë°í¸®Áò¿¡¼´Â ±âÁ¸ÀÇ À¯Æ¿¸®Æ¼ ¼øÂ÷ÆÐÅÏ ¸¶ÀÌ´× ±â¹ý¿¡¼ À¯È¿ÇÑ °á°ú·Î °£ÁֵǾú´ø ¼øÂ÷ÆÐÅÏÀ̶ó ÇÏ´õ¶óµµ ¾ÆÀÌÅÛ °£ ½Ã°£°£°ÝÀÌ Å« Áß¿äÇÏÁö ¾ÊÀº ÆÐÅϵéÀ» Á¦¿ÜÇÔÀ¸·Î½á À¯È¿ÇÑ ¼øÂ÷ÆÐÅÏÀ» ã¾Æ³½´Ù. ½ÇÇè °á°ú, Á¦¾ÈÇÑ ¾Ë°í¸®ÁòÀº ±âÁ¸ÀÇ ±â¹ý¿¡ ºñÇØ ½Ã°£Àû Ãø¸é¿¡¼ À¯È¿ÇÏÁö ¾ÊÀº ¼øÂ÷ÆÐÅϵéÀ» ÀûÀýÈ÷ °ËÃâÇÏ¿© Á¦¿ÜÇÔÀ¸·Î½á Çö½ÇÀûÀ¸·Î È¿¿ë¼º ÀÖ´Â ¼øÂ÷ÆÐÅϵéÀ» µµÃâÇÏ¿´´Ù.
¿µ¹®³»¿ë
(English Abstract)
Sequential pattern mining methods using the frequency and quantity of items have been studied, and recently, utility sequential pattern mining methods based on a utility index are actively investigated, which also consider the profit of items to find effective sequential patterns. However, the existing utility-based methods are not able to handle various types of sequential patterns in reality as they do not consider the time interval between occurrences of items. Noticing these problems, we present a new index that considers the time interval in addition to current indices. The proposed algorithm is designed to recognize the effective sequential patterns that reflect the time value by filtering the patterns with little or weak relevance, due to long-time interval between the items. Experimental results demonstrate that the proposed method identifies the relevant sequential patterns more effectively than the traditional sequential pattern mining and utility-based methods, by eliminating the patterns that have weak relevance in terms of time intervals.
Å°¿öµå(Keyword)
µ¥ÀÌÅÍ ¸¶ÀÌ´×
¼øÂ÷ÆÐÅÏ ¸¶ÀÌ´×
À¯Æ¿¸®Æ¼ ¼øÂ÷ÆÐÅÏ
½Ã°£°£°Ý À¯Æ¿¸®Æ¼
data mining
sequential pattern mining
utility sequential pattern
time-interval utility
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