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12¿ù 21ÀÏ(¸ñ) ¿ÀÈÄ 5½ÃºÎÅÍ ÀΰøÁö´É¿¬±¸È¸ ±âÃʱ׷ì¸ðÀÓÀÌ ÀÖ½À´Ï´Ù. ¸¹Àº Âü¼®ºÎŹµå¸³´Ï´Ù. (À̹øÁÖ ¸ñ¿äÀÏÀÔ´Ï´Ù. ²À Ç¥½ÃÇصνñ⠹ٶø´Ï´Ù.) À̹ø ¸ðÀÓÀÇ ¼¼¹Ì³ª °»ç·Î´Â Á¤º¸Åë½Å´ëÇпø´ëÇб³ÀÇ À̺¹ÁÖ ±³¼ö´Ô²²¼ ¼ö°íÇØÁֽðڽÀ´Ï´Ù. ÀϽÃ: 2000³â 12¿ù 21ÀÏ (¸ñ) 5:00 pm - Àå¼Ò: ´ë¿ìÀç´Üºôµù (¼¿ï¿ª ¸ÂÀºÆí) 8Ãþ ¼¼¹Ì³ª 1½Ç ÀüÈ: 02-6366-0033 (ÇмúÇùÀÇȸ) ** ÁÖÂ÷±ÇÀº ¹ß±ÞÀÌ ¾ÈµÈ´Ù°í ÇÕ´Ï´Ù. Àú³á½Ä»ç¿Í ´õºÒ¾î "¼Û³â¸ðÀÓ"µµ °âÇÏ°íÀÚ ÇÏ¿À´Ï µÉ ¼ö ÀÖÀ¸¸é Â÷´Â °¡Á®¿ÀÁö ¸¶½Ã±â ¹Ù¶ø´Ï´Ù. °»ç: À̺¹ÁÖ ±³¼ö´Ô (ICU) ¼¼¹Ì³ª Á¦¸ñ: Text Mining Technique Overview and an Application to Customer E-mail Routing System Abstract: Text mining is a branch of data mining that has been used to text categorization, information filtering, information agents, and WWW. It differs from the data mining in that it deals with unstructured or semi-structured data (i.e., documents). The data typically consist of words where the numbers are quite large. The words in the text mining are viewed as features in the data mining. Text mining and data mining differs also in that text mining handles a lot of features. Many approaches that are used in the text mining came from machine learning techniques such as decision trees and Bayesian networks. It is also strongly linked to the information retrieval theory and statistics. Recall, precision, and TFIDF are those examples. In this talk, we go through the various techniques that have been used in text mining. The applications and systems in text mining area are presented too. Finally an application of text mining to the customer e-mail routing system is presented. ¼¼¹Ì³ª °»ç ¾à·Â: À̺¹ÁÖ ±³¼ö 1982.3 - 1986.2 ¼¿ï´ëÇб³ ÄÄÇ»ÅÍ°øÇаú Çлç 1987.12 - 1990.8 »ï¼ºµ¥ÀÌŸ½Ã½ºÅÛ ¿¬±¸¿ø 1990.8 - 1992.6 South Carolina ´ëÇб³ ÄÄÇ»ÅÍÇÐ ¼®»ç 1992.8 - 1996.12 Texas A&M ´ëÇб³ ÄÄÇ»ÅÍÇÐ ¹Ú»ç (Genetic Algorithms) 1997.3 - 2000.1 AT&T Labs. Senior Member of Technical Staff 2000.2 - ÇöÀç Çѱ¹Á¤º¸Åë½Å´ëÇпø´ëÇб³ Á¶±³¼ö |
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