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Tutorial |
Recent Advance in Data
Algorithms for Large Databases |
Kaist ½É±Ô¼® ±³¼ö (Çѱ¹°úÇбâ¼ú¿ø
Àü»êÇаú) |
Machine Learning for
Web/Text/Bio Data Mining |
¼¿ï´ë À庴Ź ±³¼ö (¼¿ï´ë ÄÄÇ»ÅͰøÇаú) |
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Æú¸®¸¶½º ¼º´É Çâ»óÀ» À§ÇÑ ºÎ½ºÆÃ ±â¹ýÀÇ
ÀÀ¿ë |
±¸ÀÚ¿ë(ÇѸ²´ë), ±èÀ¯¿ø, ÀüÁ¾¿ì(¼¿ï´ë) |
Improving Adaboost via
dimension reduction |
±è¿ë´ë,
±èÁø¼®, ±èÀ¯¿ø, ÀüÁ¾¿ì(¼¿ï´ë) |
G-CLUS Àη ¸ðµ¨¿¡ ±â¹ÝÇÑ ÀÚ¿¬Àû
±ºÁýÈ ±â¹ý |
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°íÀçÇÊ, º¯Çý¶õ, ÀÌÀϺ´(¿¬¼¼´ë) |
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°èÃþÀû °í¼Ó Ŭ·¯½ºÅ͸µ ¹æ¹ý |
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±èÅüö(LGÀüÀÚ±â¼ú¿ø) |
°èÃþÀû ÇнÀ ¸Þ¸ð¸® ±â¹ÝÀÇ ¿¬°üÁö½Ä
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½ÉÁ¤¿¬,
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¡ß
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Tutorial |
Financial Engineering
and Data Mining |
±¸Çü°Ç(¾ÆÁÖ´ë) |
°³ÀÎÀÚ»ê°ü¸®¿¡¼ÀÇ µ¥ÀÌÅ͸¶ÀÌ´× |
±¸ÀÚ¿ë(ÇѸ²´ë) |
»ç·Ê¹ßÇ¥ |
ACF¾Ë°í¸®ÁòÀ» ÅëÇÑ °³¹ß»ç·Ê (Smart-RS
°³¹ß»ç·Ê) |
ÁöÅÂâ(LG-EDS) |
ILOG Business Rule ½Ã½ºÅÛ¿¡
ÀÇÇÑ ±ÝÀ¶ ¼Ö·ç¼Ç
- Intelligence Banking
& Finance e-Business Application |
±¸±³¿¬(KSTec) |
Introduction to Data
Mining Applications Development |
¹Ú¸íÀº(Microsoft) |
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SOMÀ» ÀÌ¿ëÇÑ KOSPI200 ¼±¹°
µ¥ÀÌÅÍÀÇ ½Ã°¢Àû Ž»ö |
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Çù·ÂÀû ÃßÁøÀ» À§ÇÑ ½Å°æ¸Á Á¢±Ù ¹æ¹ý |
µµ¿µ¾Æ,±èÁ¾¼ö,·ùÁ¤¿ì,±è¸í¿ø(¼þ½Ç´ë) |
Risk management with Wavelet-based
Value-at-Risk
and Expected Shortfall |
À̱¤Ãá(¿¬¼¼´ë) |
³ªÀÌºê º£ÀÌÁö¾È ³×Æ®¿÷À» ÀÌ¿ëÇÑ ÀüÀÚ¸ÞÀÏ ºÐ·ù |
Ȳȣ¼ø,±Ç¿µ½Ä(µ¿±¹´ë) |
data miningÀ» ÀÌ¿ëÇÑ °í°´º°
portfolio ±¸¼º |
¹ÚÇåÁø(ÀÎÇÏ´ë),ÃÖ´ë¿ì(Çѱ¹¿Ü´ë) |
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À±Á¾½Ä,±Ç¿µ½Ä(µ¿±¹´ë) |
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»ç·Ê±â¹Ý Ãß·ÐÀ»ÀÌ¿ëÇÑ ÅØ½ºÆ® ¸¶ÀÌ´× |
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ºÐ·ù ÆÐÅÏ ¼±Åà |
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´Ù´Ü°è ÀϹÝÈ ¼±Çü¸ðÇüÀ» Àû¿ëÇÑ »õ·Î¿î
±â¾÷µµ»ê ¿¹Ãø¸ðÇü |
³ë¸Í¼®(¿¡½º¸µÅ©),Áö¿øÃ¶(È«ÀÍ´ë), ÀÌ¿µÁ¶(¼¿ï´ë) |
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À§ÇÑ È¿À²ÀûÀÎ ¾Ë°í¸®Áò |
½É±Ô¼®,Çöµ¿ÁØ(KAIST) Minos Garofalakis Rajeev Rastogi
(Bell lab) |
º£ÀÌÁö¾È ¸Á¿¡ ±âÃÊÇÑ °¡ÀÓ ¿¹Ãø |
ÁøÈÆ,±èÀÎö(°æ±â´ë) |
À¥ ·Î±× ÈÀÏ¿¡¼ ¼øÈ¸ ÆÐÅÏ Å½»ç ¾Ë°í¸®Áò |
¹ÚÁ¾¼ö,ÇϹ̶ó(¼º½É¿©´ë) |
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¡ß 2001³â
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Invited
Talk |
Analytical CRM for Telecom
Industry The WAR Approach |
Paulo Werneck Costa
(IBM) |
Enterprise-wide CRM
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Á¤¼öõ(»ï¼ºÄ«µå) |
Tutorial |
Semi-supervised learning |
ÀÌâȯ (µ¿±¹´ë) |
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»ç·Ê¹ßÇ¥ |
ÅëÇÕ
Web BI
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ä³Î ÅëÇÕ Ä·ÆäÀΰü¸® ±¸Ãà »ç·Ê |
Á¤¼º¿í(À§¼¼¾ÆÀÌÅØ) |
Embedding the Data Mining
Process in CRM Application |
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Áõ±Ç»ç °í°´ºÐ¼® »ç·Ê¿¬±¸ |
±èÁöÇö(À¯´Ïº¸½º) |
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½Ã½ºÅÛ ±¸Ãà »ç·Ê |
ÀÌÁ¾¼®(»ï¼ºSDS) |
Åë½Å¾÷¿¡¼ÀÇ µ¥ÀÌÅÍÆ¯¼º ºÐ¼®°ú gCRM ºÎ°¡¼ºñ½º °³¹ßÀü·« |
Àå¿øÈ£,
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ÀüÀÚ»ó°Å·¡ »óǰÃßõÀ» À§ÇÑ ºÐ¼®¹æ¾È |
ÇöÁø¼®
(DMS Lab) |
¼Ò±â¾÷ÀÇ ½Å¿ë ¸®½ºÅ© ºÐ¼®°ú ´ëÃâÇѵµ
°áÁ¤ |
Á¶È«±Ô(Çѱ¹½Å¿ëÁ¤º¸) |
Áö¿ª½ÃÀå»ó±ÇÀÇ »óǰº° ±¸¸ÅÀáÀç·Â ¿¹Ãø
¹× gCRMÀÇ Àü·«Àû Ȱ¿ë |
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Ŭ·¹¸àŸÀÎÀ» ÀÌ¿ëÇÑ ÀÀ¿ë¸ðÇü |
¾ç°æ¼÷(SPSS) |
Analytical+Operational=
Total CRM, ¿À¶óŬ ¼Ö·ç¼Ç ¹× »ç·Ê |
Á¤ÀϱÙ(¿À¶óŬ) |
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±â¹ÝÇÑ ´ÙÁß ºÐ·ù±â ½Ã½ºÅÛ |
±èÀºÁÖ,
ÀÌÀϺ´ (¿¬¼¼´ë) |
Sequence Prediction
for Novel Proteins by Mining Sequence Databases |
Á¶¼øÀÌ(Àü³²´ë),À̵µÇå(KAIST), Á¶±¤ÈÖ(NIH), ¿ø¿ë°ü(Àü³²´ë) |
A Density-based Clustering
Method |
¾È¼º¸¸(±¹¹Î´ë), ¹é¼º¿í(Datemat Systems Research Inc) |
An Up-Trend Detection
Using an Auto-Associative Neural Network
: KOSPI 200 Futures |
¹éÁø¿ì, Á¶¼ºÁØ(¼¿ï´ë)
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´ÙÂ÷¿ø ¿¬¼ÓÆÐÅÏÀÇ ¸¶ÀÌ´×¹æ¹ý |
À̼ҹÎ,
ÀÌâȯ(µ¿±¹´ë) |
Decision Boundary Pattern
Selection for Support Vector Machines |
½ÅÇöÁ¤,
Á¶¼ºÁØ(¼¿ï´ë) |
Àڱ⿬»ó ´ÙÃþÆÛ¼ÁÆ®·ÐÀÇ ÀÌ»ó ŽÁö ¼º´É¿¡
´ëÇÑ ½ÇÇè |
ÀÌÇüÁÖ(¼¿ï´ë), Ȳº´È£(LG), Á¶¼ºÁØ(¼¿ï´ë) |
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Tutorial |
Making Scorecards for
Credit Scoring |
ÃÖ´ë¿ì
(Çѱ¹¿Ü±¹¾î´ë) |
OLAP
Technology ÀÇ ÀÌÇØ |
Á¶ÀçÈñ
(±¤¿î´ë) |
Keynote Speech |
Passenger-Based Predictive
Modeling of Airline No-show Rates |
È«¼¼ÁØ
(IBM/KAIST) |
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µ¥ÀÌÅ͸¶ÀÌ´×À»
Ȱ¿ëÇÑ ¸¶ÄÉÆÃÀü·« ¼ö¸³ |
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Data
Mining Strategy Planning
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(Agenda Korea)
ÀÌÀç½Ä
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Value
Chain°ü¸®¸¦ À§ÇÑ data mining Ȱ¿ë ¹æ¾È |
À̵¿Çö (SAS
ÄÚ¸®¾Æ) |
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Á¤·®Àû ¿¬°ü±ÔÄ¢±â¹ýÀ»
ÀÌ¿ëÇÑ À̵¿Åë½Å¾÷üÀÇ ºÎ°¡¼ºñ½º ÀÌ¿ëÆÐÅÏ ÆÄ¾Ç |
¿ìÁ¦Çö, ¼Õ¼Ò¿µ (¿¬¼¼´ë) |
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ÀÚµ¿Â÷¾÷°è
°í°´°¡Ä¡ ¹× À籸¸Å °¡¸Á°í°´ ¸ðµ¨¸µÀ» Ȱ¿ëÇÑ ¸¶ÄÉÆÃ »ç·Ê |
¼¿ë´ö(À¯´Ïº¸½º¾ÆÀÌÁ¨ÅØ) |
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ASPÇ÷§Æû±¸Ãà»ç¾÷¿¡¼ ¸¶ÀÌ´×ÀÇ ¿ªÇÒ |
Á¤¼º¿ø (SPSS) |
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ROIÇâ»óÀ»
À§ÇÑ Äݼ¾ÅÍ ÃÖÀûÈ |
ÀÌÇö¿ì (½Ã½ºÅÛºñÁö´Ï½º) |
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Åë½Åȸ»ç
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ÀÓ¿ë¾÷,ÀÌÁظí (Marketing
Lab) Á¶¼ºÁØ
(¼¿ï´ë) |
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¹ýÁýÇà±â°ü¿¡¼ÀÇ Link Analysis Technique Àû¿ë»ç·Ê |
¹è¹®ÁØ (eCentric) |
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º¸Çè»çÀÇ ¿Â¶óÀÎ°í°´ ´ÙÂ÷¿øºÐ¼® ¹× ½ºÄھ ½Ã½ºÅÛ »ç·Ê |
À±Á¤ÈÆ (À§¼¼¾ÆÀÌÅØ) |
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µ¥ÀÌÅ͸¶ÀÌ´×À»
À§ÇÑ ½Å°æ¸Á ÀÌ¿ë °áÃø°ª ¿¹Ãø ¹æ¹ý
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¼ºÁö¾Ö, ·ùÁ¤¿ì, ±è¸í¿ø (¼þ½Ç´ë) |
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±è¿øÈ, ÀÌÀϺ´ (¿¬¼¼´ë) |
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Strengthen Keystroke Dynamics Identity
Verification Capability via Ensemble based
on Feature Selection |
¿ìÀºÃ¶, Á¶¼ºÁØ (¼¿ï´ë) |
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Fast
Pattern Selection Algorithm for Support
Vector Classifiers: Time Complexity Analysis
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½ÅÇöÁ¤, Á¶¼ºÁØ (¼¿ï´ë) |
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Combining both ensemble
and dynamic classifier selection schemes
for prediction of mobile internet subscribers
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½ÅÇü¿ø, ¼Õ¼Ò¿µ (¿¬¼¼´ë) |
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´ÙÁß ±¸Á¶ÀûÀÀ Àڱⱸ¼ºÁöµµÀÇ ÆÛÁö°áÇÕ: À¥
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±è°æÁß, Á¶¼º¹è (¿¬¼¼´ë) |
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ÀÎ½Ä ¼º´É Çâ»óÀ» À§ÇÑ ¾ó±¼ ¿µ»ó ±ºÁýÈ ±â¹ý |
±ÇÇý·Ã, °íº´Ã¶, º¯Çý¶õ, ÀÌÀϺ´
(¿¬¼¼´ë) |
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½Å°æÈ¸·Î¸Á ¹× SVMÀ» ÀÌ¿ëÇÑ
KOSPI200 Áö¼ö¿¡ ´ëÇÑ ´ÙÁ߯ÐÅϺзù |
À̼ö¿ë, ÀÌÀϺ´ (¿¬¼¼´ë) |
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SOMÀ»
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À̰üÈñ, ÀÌÀϺ´ (¿¬¼¼´ë) |
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»ýü½ÅÈ£ÀÇ Æ¯Â¡ÃßÃâ ¹× ½Å°æ¸Á
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¡ß
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½Å¿ëÄ«µåºÎÁ¤¹æÁö½Ã½ºÅÛ Àû¿ë»ç·Ê |
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¿¹Ãø ¸ðÇü°³¹ß-Çö´ëijÇÇÅ» »ç·Ê |
ÀÌ¿¬¼ö(¹Ì·¡½Å¿ëÁ¤º¸) |
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KIS bureau data¿Í bureau
model¿¡ °üÇÑ ¼Ò°³ |
ÀÌÁÖÈñ(Çѱ¹½Å¿ëÆò°¡Á¤º¸) |
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±èÁ¾À±(Çѱ¹½Å¿ëÁ¤º¸) |
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Using structural changes
to build up clustering neural networks reflecting
the time structure |
¿À°æÁÖ(ÇѼº´ë), ¹®¸í»ó(¿¬¼¼´ë) |
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Information-theoretic
clustering through quadratic distance measure
between probability densities |
ÀÌ¿ëÁø,
ÃÖ½ÂÁø(Æ÷Ç×°ø´ë) |
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À¥ÄÁÅÙÃ÷ÀÇ ±¸Á¶Àû ¿¬°ü¼ºÀ» °í·ÁÇÑ ±ºÁýºÐ¼®
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°Çöö, ÇÑ»óÅÂ(È£¼´ë) |
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½ºÇöóÀÎÀ» ÀÌ¿ëÇÑ ½ºÄ¿¾îÄ«µå |
Ãֹμº,
±¸ÀÚ¿ë(ÀÎÇÏ´ë)
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How to deal with large
dataset, class imbalance and binary output
in SVM based response model |
½ÅÇöÀúÀ¸ Á¶¼ºÁØ(¼¿ï´ë) |
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½Å¿ëÆò°¡¸ðÇü¿¡¼ÀÇ °ÅÀýÀÚ Ãß·Ð |
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Segmentation-driven
customer purchase prediction: an empirical
study of an automaker |
Àü¿ëÁØ(À¯´Ïº¸½º¾ÆÀÌÁ¨ÅØ), Ȳ¼º¼ø(Çö´ëÀÚµ¿Â÷), ¹ÎÁ¤½Ä,ÇãÁ¤Èñ,±è³²°æ(Autoever Sys.) |
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Gaussian mixture modelÀÇ
committee °áÇÕ¹æ¹ý |
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»óǰº° ±¸¸ÅÆÐÅÏÀ» ÀÌ¿ëÇÑ ÇÁ·ÎÆÄÀÏ ±â¹Ý
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Tutorial |
Business Intelligence:
The next step of BPM Evolution |
¹ÚÁ¾Çå
(¼¿ï´ë) |
Statistical Process
Control for Data Quality |
ÃÖ´ë¿ì
(¿Ü±¹¾î´ë) |
Bioinformatics and Applied Genomics |
±èÁÖÇÑ (¼¿ï´ë ÀÇ´ë) |
Multi-category Support
Vector Machine |
±¸ÀÚ¿ë
(°í·Á´ë) |
Keynote |
Introducing CRM/Risk Management based
on Decision Science: Overcoming Cultural
Barriers |
ÀåÂù
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¼ÀÏÁ¤,Á¶ÀçÈñ (±¤¿î´ë) |
±¹¹ÎÄ«µå
°í°´ ¼¼ºÐÈ |
Áø¼ÈÆ (±¹¹ÎÄ«µå) |
Business Intelligence
Àû¿ë ¹æ¾È |
¹Ú¼¼°æ ÀÌ»ç (ÆæÅ¸½Ã½ºÅÛÅ×Å©³î·ÎÁö BI»ç¾÷ºÎ) |
Á¦Á¶¾÷¿¡¼ ECMiner¢â Ȱ¿ë»ç·Ê |
¹Î±¤±â (EC Miner) |
¼º°øÀûÀÎ BI ½Ã½ºÅÛ µµÀÔÀ» À§ÇÑ Ç°Áú°ü¸® ½Ã½ºÅÛ ±¸Ãà |
ÃÖ»óºÀ (Çѱ¹¼öÃ⺸Çè°ø»ç) |
(*) ´Ù¹üÁÖ ºÐ·ù
¹®Á¦¿¡ ´ëÇÑ SVM ¹æ¹ýÀÇ ºñ±³ ¿¬±¸ |
ÃÖÇýÁ¤, ÀÌ»óÁØ (¼¿ï´ë) |
Support Vector
Machine for Interval Regression with Crisp
Input-Output |
ȲâÇÏ (´ë±¸Ä«Å縯´ë) |
ÀÇ»ç°áÁ¤³ª¹«¿¡¼ SVMÀ» ÀÌ¿ëÇÑ ºÐ¸®Á¡ ¼±Åà |
ÇѰ¹Î, ±èÇöÁß (¿¬¼¼´ë) |
Data reduction
for Support Vector Regressor |
¼±Áö¿µ, Á¶¼ºÁØ (¼¿ï´ë) |
µ¶¸³¼ººÐºÐ¼® ¹æ¹ýµéÀ»
ÀÌ¿ëÇÑ microarray ½Ã°è¿µ¥ÀÌÅÍ ºÐ¼® |
±è¼÷Á¤, ÃÖ½ÂÁø (Æ÷Ç×°ø´ë) |
Protein Functional
Class Prediction with a Combined Kernels |
Shin, Tsuda,
Schoelkopf
(¸·½ºÇöûÅ©¿¬±¸¼Ò, ¼¿ï´ë) |
Predicting
subcellular localization of proteins using
pairwise sequence alignment and support
vector machine |
±èÁ¾°æ, Raghava, ±è±¤¼ö, ¹æ½Â¾ç, ÃÖ½ÂÁø, (Æ÷Ç×°ø´ë) |
Discovering
significant and interpretable patterns from
multifactorial DNA microarray data with
poor replication |
±èÁÖÇÑ(¼¿ï´ë) |
Gene Ontology¸¦
ÀÌ¿ëÇÏ¿© À¯ÀüÀÚ ¹ßÇö Ŭ·¯½ºÅÍÀÇ ÀÚµ¿ annotation ȤÀº ÇØ¼® |
±è¾ç¼® (ÀÌÁîÅØ) |
µ¥ÀÌÅÍǰÁú°ú µ¥ÀÌÅÍ
¼º¼÷µµ ¸ðÇü |
¹ÚÁÖ¼®, ±èÂù¼ö, ¹®Á¾¼º (°æÈñ´ë) |
Data Quaility
Management ÀÚµ¿È¹æ¾È |
ÀÌ¿ë¿ì (Ascential Korea) |
µ¥ÀÌÅÍǰÁú º¸ÀåÀ»
À§ÇÑ ¸ÞŸµ¥ÀÌÅÍ °ü¸® µµÀÔ |
±èÀÎâ (ÅõÀÌÄÁ¼³ÆÃ) |
µ¥ÀÌÅÍ Ç»Àü°ú Æò°¡ |
ÇÑ»óÈÆ, ¾ÈÀÏÈ£, ÇÏ´öÁÖ, ÃÖÁ¾ÈÄ (°í·Á´ë) |
Àü¿ªÀû ¹üÁÖÈ¿¡ ÀÇÇÑ
°áÁ¤Æ®¸® »ý¼ºÀ» À§ÇÑ ¼øÂ÷Àû ±â¹ý
|
ÇѰæ½Ä, À̼ö¿ø (¼þ½Ç´ë) |
Ensemble Technique
for Data Imbalance Problem |
°Çʼº, Á¶¼ºÁØ, ÀÌÇüÁÖ (¼¿ï´ë) |
À¥ IRÀ» À§ÇÑ ¿µ¾î ¿Ü·¡¾î Á¤ÇÕ¹æ¹ý |
ÀÌÁøÈ£, ±èÇÏ¿ë, ¾Èâȯ, º¯¿µÅ (È«ÀÍ´ë) |
È¿À²ÀûÀΠħÀÔŽÁö½Ã½ºÅÛ
(IDS) ÀÇ ¾Ë¶÷ ·Î±× ºÐ¼® Á¦¾È |
Á¤ÀÎö, ±Ç¿µ½Ä (µ¿±¹´ë) |
Gradient LASSO
with application to gene expression data
analysis |
±èÁø¼®, ±èÀ¯¿ø, ±è¿ë´ë (¼¿ï´ë) |
Learning vector
quantization for Novelty Detection |
ÀÌÇüÁÖ, Á¶¼ºÁØ (¼¿ï´ë) |
new pruning
algorithm for decision tree |
±èÁø¼®, ±è¿ë´ë, ÀüÁ¾¿ì (¼¿ï´ë) |
¼±°Å¿¹ÃøÁ¶»ç ÀÇ»ç°áÁ¤
À¯º¸Ãþ ºÐ·ù ¹× ¿¹ÃøÀ» À§ÇÑ ÀÇ»ç°áÁ¤³ª¹«¸ðÇüÀÇ ºñ±³¿Í Æò°¡ |
ÃÖÁ¾ÈÄ, °Çöö, ÇÏ´öÁÖ, ÇÑ»óÈÆ (°í·Á´ë) |
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¡ß
2005³â Ãß°èÇмú´ëȸ
¡ß
ÀϽÃ: 2005³â
12¿ù 3ÀÏ(±Ý)
¡ß
Àå¼Ò: Çѱ¹°úÇбâ¼úȸ°ü |
|
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Keynote |
½ÅBIS
Á¦µµÀÇ ³»¿ë ¹× ´ëÀÀ¹æ¾È |
ÀÓ Ã¶¼ø
(±ÝÀ¶°¨µ¶¿ø, ÆÀÀå) |
Tutorial |
Building best practice
retail portfolio credit risk management |
±è Çö±â
(¹Ì·¡½Å¿ëÁ¤º¸, experian-scorex) |
½ÅBIS
Çù¾à Áؼö¸¦ À§ÇÑ EAD, LGD ÃßÁ¤ - À̽´¿Í ´ë¾È |
Á¤ ¿ìö
(KPMG)
|
Basel-II ½Å¿ë¸®½ºÅ© ÃøÁ¤¸ðÇü°ú
t-copular ±â¹Ý¸ðÇüÀÇ ºñ±³ºÐ¼® ¹× ½ºÆ®·¹½º Å×½ºÆ® |
À̱ºÈñ
(¼°´ë) |
Çö´ëÀûÀÎ
BIÀÇ ÀÇ¹Ì¿Í Â÷¼¼´ë Á¤º¸°èÀÇ ±¸Á¶ |
À嵿ÀÎ
(¸¶ÄÉÆÃ·¦) |
Basel-II |
Basel-II ´ëÀÀÀ» À§ÇÑ System
Implementation ¹æ¾È |
±è ±â¿µ
(LG-CNS) |
Basel-II¿¡¼ÀÇ µ¥ÀÌÅ͸¶ÀÌ´× |
ÀÌÁÖö
(SAS ÄÚ¸®¾Æ) |
µ¥ÀÌÅÍǰÁú°ü¸®Ã¼°è µµÀÔ¹æÇâ
|
ÀÌ ¿ë¿ì
(IBM Korea, Senior IT Architect, Professional
Services Team Leader) |
¾¾Æ¼ÀºÇàÀÌ ¼±ÅÃÇÑ Basel-II ¼Ö·ç¼Ç Reveleus |
½Åµ¿¿±
(Çѱ¹¿À¶óŬ, ±ÝÀ¶»ç¾÷º»ºÎ ºÎÀå) |
³í¹®¹ßÇ¥ |
±â¼ú À¶ÀÚ »ç°í¿¹ÃøÀ» À§ÇÑ °æÀïÀ§Çè¸ðÇü |
ÀüÇýÁø,
¼Õ¼Ò¿µ (¿¬¼¼´ë) |
SVA ¾Ó»óºíÀ» ÀÌ¿ëÇÑ »ç±â ŽÁö
|
±èÇöö
(Æ÷Ç×°ø´ë), Shaoning Pang (Auckland Univ. of
Tech.), ±è´ëÁø, ¹æ½Â¾ç (Æ÷Ç×°ø´ë) |
ÁֽĽÃÀå¿¡¼ÀÇ ºÒ°øÁ¤ ¸Å¸Å ÀûÃâ ¸ðÇü¿¡
°üÇÑ ¿¬±¸ |
±èÁ¤ÈÆ
(¿¬¼¼´ë), ±èÀ±¼º, ¼Õ¼Ò¿µ (¿¬¼¼´ë) |
Â÷¿øÃà¼Ò¸¦ ÅëÇÑ ¿Â¶óÀÎ ¹®¼ºÐ·ù ½Ã½ºÅÛ
|
ÁöÅÂâ
(¿¬¼¼´ë), ÀÌÇöÁø (Çѱ¹½ÎÀ̹ö´ë), ÀÌÀϺ´ (¿¬¼¼´ë) |
AWS °üÃøÀڷḦ Ȱ¿ëÇÑ ±â»ó°üÃøÀÚ·á
½Ç½Ã°£ ǰÁú°ü¸® ½Ã½ºÅÛÀÇ Ç°Áú°Ë»ç |
³ë¸Í¼®,
ÀÌ¿µÁ¶ (¼¿ï´ë) |
Ž»öÀû Áö½Ä»ý¼ºÀ» À§ÇÑ µ¥ÀÌÅÍ ½Ã°¢Àû
ºÐ¼® ÇÁ·Î¼¼½º |
¼ÀÏÁ¤,
Á¶ÀçÈñ (±¤¿î´ë)
|
Keystroke dynamics-based
authentication with pauses and cues |
Ȳ¼º¼·,
ÀÌÇüÁÖ, Á¶¼ºÁØ (¼¿ï´ë)
|
Ű½ºÆ®·ÎÅ© ±â¹Ý »ç¿ëÀÚ ÀÎÁõ¿¡¼ÀÇ Àΰø¸®µë°ú
ÅÛÆ÷ Å¥ÀÇ È¿°ú |
°Çʼº,
¹Ú¼ºÈÆ, Á¶¼ºÁØ, Ȳ¼º¼·, ÀÌÇüÁÖ (¼¿ï´ë) |
Trajectory-based support
vector classifier for multi-class classification
problems |
ÀÌ´ë¿ø,
ÀÌÀç¿í (Æ÷Ç×°ø´ë) |
Support vector regression¿¡¼ÀÇ
¥å-tube ±â¹Ý ÇнÀ ÆÐÅÏ ¼±Åà |
±èµ¿ÀÏ,
Á¶¼ºÁØ (¼¿ï´ë) |
ANOVA-Boosting |
±è¿ë´ë,
±èÀ¯¿ø, ±èÁø¼® (¼¿ï´ë) |
Multiclass sparse logistic
regression for classification of multiple
cancer types using gene expression data |
±Ç¼ºÈÆ,
±è¿ë´ë (¼¿ï´ë), ¼Û¼®Çå (°í·Á´ë) |
¹üÇÎÀ» ÀÌ¿ëÇÑ ½ºÇöóÀÎ ½ºÄÚ¾îÄ«µå ¼º´ÉÇâ»ó
|
À̹̿¬
(°í·Á´ë), À¯¿ø±Ù (SCÁ¦ÀÏÀºÇà Decision
ScienceÆÀ) |
ÅØ½ºÆ®¿Í À̹ÌÁö¸¦ ±â¹ÝÀ¸·ÎÇÑ À̸ÞÀÏ
ÇÊÅ͸µ ±â¹ý |
±è¿øÈ,
ÀÌÀϺ´ (¿¬¼¼´ë) |
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¡ß
2006³â Ãß°èÇмú´ëȸ
¡ß
ÀϽÃ: 2006³â
11¿ù 11ÀÏ(Åä)
¡ß
Àå¼Ò: µ¿±¹´ëÇб³
¸¸Çذü |
|
|
Tutorial |
Biologically Inspired
Computing for Data Mining and Knowledge
Discovery |
À庴Ź(¼¿ï´ë) |
Cancer Classification
Methods Using Microarray Data |
Á¶¼º¹è(¿¬¼¼´ë) |
Çѱ¹Åë½Å»ê¾÷°ú CRM |
ÀüÈñÁÖ(SK ÅÚ·¹ÄÞ Biz ºÐ¼®ÆÀ) |
ÆÐ³ÎÅäÀÇ
|
»çȸ:
Á¶¼ºÁØ(¼¿ï´ë »ê¾÷°øÇаú)
ÆÐ³Î¸®½ºÆ®
: ¹Î±¤±â»çÀå(ECminer »çÀå), ÀÌ´ö±â¹Ú»ç(»ï¼º Tesco), ÀÌÀç¿í±³¼ö(Æ÷Ç×°ø´ë
»ê¾÷°æ¿µ°øÇаú), ÀüÈñÁÖ¹Ú»ç(SK ÅÚ·¹ÄÞ Biz ºÐ¼®ÆÀ ºÎÀå), ÃÖÁ¤È¯¹Ú»ç(LG°æÁ¦¿¬±¸¼Ò) |
»ç·Ê¹ßÇ¥ |
½Å¿ëÄ«µåÀÇ
CRM »ç·Ê |
Àå¼ø°ï(½ÅÇÑÀºÇà) |
ÀÏ¹Ý³í¹® |
¿Â¶óÀÎ ¹®¼ ±ºÁýȸ¦ À§ÇÑ ±ºÁý °³¼ö
Ãà¼Ò |
ÁöÅÂâ(¿¬¼¼´ë), ÀÌÀϺ´(¿¬¼¼´ë) |
k-ÀÎÁ¢ ÀÌ¿ô ºÐ·ùÀÇ ¼öÇà ¼Óµµ Çâ»óÀ»
À§ÇÑ ÂüÁ¶ µ¥ÀÌÅÍ ¼Â ¾ÐÃà |
¹ÚÁø¿ì,
Á¶¼ºÁØ(¼¿ï´ë)
|
Graph Based Semi-Supervised
Learning with Sharper Edges |
½ÅÇöÁ¤(¾ÆÁÖ´ë), N.Jeremy Hill
(Friedrich Miescher
Laboratory), Gunnar Räatsch(Max Planck
Institute) |
Towards XML Mining:
The Role of Kernel Methods |
Á¤ºÎȯ,
ÀÌ´ë¿ø, ÀÌÀç¿í, Á¶Çöº¸(Æ÷Ç×°ø´ë) |
ÀÎÆ®·Ð »óŸ¦ ÀÌ¿ëÇÑ Á¾ Ưº° À¯ÀüÀÚ
¿¹Ãø : ¸Ó½Å ·¯´× ¹æ¹ý |
±èµµÇö,
±è¿ë´ë(¼¿ï´ë), ±èÈñ¹ß(¼¿ï´ë) |
Componentwisely sparse
Boosting
|
±è¿ë´ë(¼¿ï´ë), ±èÀ¯¿ø(U. of Minnesota), ±èÁø¼®(¼¿ï´ë) |
À½¼ºÀνĿ¡¼ ½Å°æ¸ÁÀ» ÀÌ¿ëÇÑ »ç¿ëÀÚ
ÀûÀÀÇü ÈÄó¸® |
±è¿µÁø,
±èÀºÁÖ, ±è¸í¿ø(¼þ½Ç´ë) |
ÀÀ´ä ¸ðµ¨¸µ¿¡¼ÀÇ »ùÇøµ ±â¹ý |
°Çʼº,
Á¶¼ºÁØ(¼¿ï´ë) |
¹®¸Æ±¤°í¿¡¼ÀÇ ¿¬°ü ÄÜÅÙÃ÷ Ãßõ±â¹ý |
°¿µ±æ,
±è¼º¹Î, À̼ºÁø, À̼ö¿ø(¼þ½Ç´ë) |
¿ÂÅç·ÎÁö ±â¹Ý ÅØ½ºÆ® ¸¶ÀÌ´× ¸®ºä |
Á¶´ë¿¬(Çѵ¿´ë) |
ÀÀ¿ë³í¹® |
È¿À²ÀûÀÎ ÁÖ¼Ò Á¤Á¦ ½Ã½ºÅÛ °³¹ß
|
¼º±â¿ë(DH µðÀÚÀÎ), ±è¼ºÀº(ǻó½Ã½ºÅÛ), ¹Ú±ÔÁø, ¼Û¿ø¹®, ±è¸í¿ø(¼þ½Ç´ë)
|
¼öµµ±Ç ¾ÆÆÄÆ® °¡°Ýµ¿ÇâÀÇ ½Ã°¢È¿Í ÆÐÅϺм®
|
À嵿ÀÍ(¼¿ï´ë), Rabindra Nath Das(U. of Bundwan), ÀÌÀç¿ë, ¿ÀÈñ¼®(¼¿ï´ë) |
±â°¢Ãß·ÐÀ» Àû¿ëÇÑ ½ÅûÆòÁ¡½Ã½ºÅÛÀÇ °³¹ß |
¾ÈÁ¤¼ö,
±Ç¿µ½Ä(µ¿±¹´ë) |
1. Á¦Á¶¾÷¿¡¼ÀÇ ECMiner Ȱ¿ë »ç·Ê |
±èÀ絿(ECMiner) |
ÄÁÅÙÃ÷¿Í ³×Æ®¿öÅ© Á¤º¸¸¦ ÀÌ¿ëÇÑ ºí·Î±×
Ãßõ ½Ã½ºÅÛÀÇ °³¹ß |
¼µ¿Ãµ,
¿°ºÀÁø(KAIST)
|
µ¥ÀÌÅ͸¶ÀÌ´× ¼öÇà½ÃÀÇ °í·Á»çÇ×µé |
¹ÚÁ¾´ö(LG-CNS Emerging Technology ÆÀ) |
´ÙÁß »ç¿ëÀÚ¸¦ À§ÇÑ DTV ÇÁ·Î±×·¥ Ãßõ ½Ã½ºÅÛ |
ÇÑÁ¤¼®,
ÀÌ¿¬Á¤, À̼ºÁø, À̼ö¿ø(¼þ½Ç´ë) |
µ¥ÀÌÅͺ£À̽º »ó¿¡¼ÀÇ µ¥ÀÌÅ͸¶ÀÌ´× |
À强¿ì(Çѱ¹ Oracle BI/DW ÆÀÀå)
|
»ç±â ¹æÁö¸¦ À§ÇÑ ¸¶ÀÌ´× ¼Ö·ç¼Ç Ȱ¿ë |
±èöȯ(SAS Korea CI ÆÀ) |
½Å¿ëÄ«µå ¸ÅÃâÁ¤º¸¸¦ Ȱ¿ëÇÑ ¼Ò»ó°øÀÎ
ºÎ½Ç¿¹Ãø¸ðÇü |
À±Á¾½Ä,
±Ç¿µ½Ä(µ¿±¹´ë) |
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¡ß
2007³â Ãá°èÇмú´ëȸ (CRM Fair 2007)
¡ß ÀϽÃ: 2007³â 4¿ù 17ÀÏ(È)
¡ß Àå¼Ò: COEX ½Å°ü 1Ãþ ±×·£µåº¼·ë
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<±âÁ¶°¿¬ 1> ±³º¸»ý¸íÀÇ CRM Àü·«
¹× »ç·Ê : Á¶ ´ç ÈÆ Á¤º¸½Ã½ºÅÛ½Ç ÆÀÀå |
< °³È¸Àλç > ÀÌ ÈÄ ¿¬ CRMÇùÀÇȸ
ȸÀå |
< Ãà »ç > ÀÌ ¿ë Á¶ Çѱ¹BIµ¥ÀÌÅ͸¶ÀÌ´×ÇÐȸ
ȸÀå |
<±âÁ¶°¿¬ 2> Analytical CRM&Success
Factors: ÀÌ ¿ë ±¸ Áß¾Ó´ë±³¼ö °â SPSS KOREA COO |
Track 1(À¯Åë/¼ºñ½º
102È£)Track 1(À¯Åë/¼ºñ½º 102È£) |
Track 2(Á¤º¸Åë½Å/e-biz, ±âŸ 103È£) |
µ¥ÀÌÅ͸¶ÀÌ´×°ú CRM
(Çѱ¹BIµ¥ÀÌÅ͸¶ÀÌ´×ÇÐȸ 104È£)
|
Pricing management°íµµÂ÷¸¦
ÅëÇÑ ¼ºÀåÀü·«¿ÀÇŸÀ̵åÄÚ¸®¾Æ(ÁÖ) |
On-demand °í°´ ¼ºñ½º¸¦ À§ÇÑ µ¥ÀÌÅÍ ¼ºñ½º ÀÎÇÁ¶ó ±¸Çö Çѱ¹»çÀ̺£À̽º(ÁÖ) |
Evolution of Data Mining
in CRMÃÖÁöÈñ(SAS KOREA) |
SOA ±â¹ÝÀÇCRM Analytics
±¸Çö SAPÄÚ¸®¾Æ |
°í°´Á᫐ °æ¿µÀ» À§ÇÑ °í°´´ÏÁî °ü¸®¹æ¾È
´ÙÀ½¼ÒÇÁÆ® |
Statistical Bioinformatics
and CollaborationÁ¶ÇüÁØ (°í·Á´ëÇб³) |
AEI (Active Enterprise
Intelligence)¸¦ Ȱ¿ëÇÑ Teradata CRMÇѱ¹NCR Å×¶óµ¥ÀÌŸ |
¾¾¾¾¹Ìµð¾î
E-CRM±â¼úÀ» Ȱ¿ëÇÑ ÀÀ¿ë ºñÁî´Ï½º »ç·Ê(ÁÖ)¾¾¾¾¹Ìµð¾î |
ºó¹ß ÆÐÅÏ ¸¶ÀÌ´×°ú ±Ù»ç°öÀ» ÀÌ¿ëÇÑ
À¯ÀüÀÚ Á¶Àý ³×Æ®¿öÅ© ¸ðµ¨¸µ ¹ÚÈ«±Ô, ÀÌÇå±Ô, ·ù±ÙÈ£(ÃæºÏ´ëÇб³) |
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°ü»óµ¿¸Æ ÁúȯÀÇ ºÐ·ù ¹× ºÐ¼®À» À§ÇÑ µ¥ÀÌÅ͸¶ÀÌ´× ±â¼úÀÌÇå±Ô, ¹ÚÈ«±Ô,
³ë±â¿ë, ·ù±ÙÈ£ (ÃæºÏ´ëÇб³) |
Coffee
Break |
Track 3 (°ø°ø/Á¦Á¶ 102È£) |
Track 4 (±ÝÀ¶ 103È£) |
µ¥ÀÌÅ͸¶ÀÌ´×°ú CRM (Çѱ¹BIµ¥ÀÌÅ͸¶ÀÌ´×ÇÐȸ104È£) |
SaaS¿Í CRMÀÇ
¹Ì·¡¼¼ÀÏÁîÆ÷½º´åÄÄ |
Making Cusotmer
FeedbackProfitableSPSS KOREAMaking Cusotmer
FeedbackProfitableSPSS KOREA |
È¿À²ÀûÀΠħÀÔ Å½Áö¸¦ À§ÇÑ ÆÛÁö ºÐ·ù ±â¹ýÀÇ ¼º´É °³¼±±è¼ºÀº, ±è¸í¿ø,
¼Û¿ø¹®, ±è¿µÁø(ǻó½Ã½ºÅÛ(ÁÖ), ¼þ½Ç´ëÇб³) |
SVMÀ» Ȱ¿ëÇÑ ½Å¿ëÄ«µå ¸ÅÃâ±â¹Ý ¼Ò»ó°øÀÎ ºÎ½Ç¿¹Ãø¸ðÇüÀ±Á¾½Ä, ±Ç¿µ½Ä
(µ¿±¹´ëÇб³) |
Á¦Á¶, Á¦Á¶»ê¾÷¿¡¼ÀÇ
¿µ¾÷·Â °È¸¦ À§ÇÑ Oracle On demand ¼Ò°³ ¹× DemoÇѱ¹
¿À¶óŬ |
CRMÀ» Ȱ¿ëÇÑ
°í°´¸ÂÃãÇü ä³Î¼ºñ½º ¼Ò°³ ¹× ±¸Ãà»ç·ÊÁÖ½Äȸ»ç ºê¸®ÁöÅØ |
À̵¿°´Ã¼ÀÇ µ¥ÀÌÅÍ ½Ã°¢È¸¦ ÅëÇÑ À̵¿ÆÐÅÏ
ºÐ¼®¿¡ °üÇÑ ¿¬±¸Á¶ÀçÈñ, ¼ÀÏÁ¤
(±¤¿î´ëÇб³) |
º¯¼ö ¼±ÅÃÀ» ÀÌ¿ëÇÑ SVM ºÐ·ù ¼º´É Çâ»ó¹ÚÀ±½Å, ±Ç¿µ½Ä (µ¿±¹´ëÇб³) |
»õÁÖ¼Ò ½ÃÇà¿¡ µû¸¥ ½Ã½ºÅÛ±¸Ãà ¹× Data
GovernanceÀü·«¼öÁö¿ø³Ý¼ÒÇÁÆ® |
±ÝÀ¶, ±ÝÀ¶»ê¾÷¿¡¼ÀÇ °í°´ µ¥ÀÌÅÍ ÅëÇÕÀ»
À§ÇÑ Oracle CDI±â´É°ú ¿ªÇÒ
Çѱ¹ ¿À¶óŬ |
µ¥ÀÌÅ͸¶ÀÌ´× ÇÐȸ Á¤±âÃÑȸ |
°æÇ°
Ãß÷ ¹× Æóȸ |
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Copyright ¨Ï 2007
Korea Data Mining Society. All rights reserved.
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