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Tutorial

Recent Advance in Data Algorithms for Large Databases

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Machine Learning for Web/Text/Bio Data Mining

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Improving Adaboost via dimension reduction

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Tutorial

Financial Engineering and Data Mining

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Introduction to Data Mining Applications Development

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Risk management with Wavelet-based Value-at-Risk

and Expected Shortfall

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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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Tutorial

Semi-supervised learning

ÀÌâȯ (µ¿±¹´ë)

 

 

 

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Embedding the Data Mining Process in CRM Application

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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

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An Up-Trend Detection Using an Auto-Associative Neural Network : KOSPI 200 Futures

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Decision Boundary Pattern Selection for Support Vector Machines

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Tutorial

Making Scorecards for Credit Scoring

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OLAP Technology ÀÇ ÀÌÇØ

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Passenger-Based Predictive Modeling of Airline No-show Rates

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Data Mining Strategy Planning

 

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¹ýÁýÇà±â°ü¿¡¼­ÀÇ Link Analysis Technique Àû¿ë»ç·Ê

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Strengthen Keystroke Dynamics Identity Verification Capability via Ensemble based on Feature Selection

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Fast Pattern Selection Algorithm for Support Vector Classifiers: Time Complexity Analysis

½ÅÇöÁ¤, Á¶¼ºÁØ (¼­¿ï´ë)

 

Combining both ensemble and dynamic classifier selection schemes for prediction of mobile internet subscribers

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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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How to deal with large dataset, class imbalance and binary output in SVM based response model

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Segmentation-driven customer purchase prediction: an empirical study of an automaker

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Gaussian mixture modelÀÇ committee °áÇÕ¹æ¹ý

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Tutorial

Business Intelligence: The next step of BPM Evolution

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Statistical Process Control for Data Quality

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Bioinformatics and Applied Genomics

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Multi-category Support Vector Machine

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Introducing CRM/Risk Management based on Decision Science: Overcoming Cultural Barriers

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Support Vector Machine for Interval Regression with Crisp Input-Output

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Data reduction for Support Vector Regressor

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±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)

 
     
 

¡ß 2005³â Ãß°èÇмú´ëȸ

¡ß ÀϽÃ: 2005³â 12¿ù 3ÀÏ(±Ý)

¡ß Àå¼Ò: Çѱ¹°úÇбâ¼úȸ°ü

 
 

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±ÅÃ

±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)

 
     
 

¡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 ±ºÁýÈ­¸¦ 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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<±âÁ¶°­¿¬ 2> Analytical CRM&Success Factors: ÀÌ ¿ë ±¸ Áß¾Ó´ë±³¼ö °â SPSS KOREA COO
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Pricing management°íµµÂ÷¸¦ ÅëÇÑ ¼ºÀåÀü·«¿ÀÇŸÀ̵åÄÚ¸®¾Æ(ÁÖ)
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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
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