Data Mining at UGent (Belgium)

 

Updated, October 25th, 2010.

 

Education

 

§        Master of Marketing Analysis

This one-year full-time program in predictive analytics (9 months from October – June) is taught in English. Students will receive classes by world-renowned experts in their field. Data-mining techniques are introduced in the application domain of analytical Customer Relationship Management (CRM)/Marketing. The main emphasis is on classification techniques (binary as well as multi-class): starting with the classical statistical techniques (e.g. logistic regression) over decision trees (including random forests) to artificial neural networks. Sufficient time is also devoted to the modeling process as such (Knowledge Discovery in Databases including the data pre-processing step) as well as checking (predictive) model quality, e.g. AUC on a test sample. There’s a separate webpage about this advanced-master degree (you already need a master degree before you can enroll). This high-quality degree is offered at the low price of € 600 ≈ 900 USD (i.e., normal full-year admission fee for all students to all ‘normal’ university degrees in Belgium) + approx. € 300 ≈ 400 USD (for books, software, …). Ghent University has been offering this high-level advanced-master degree since October 1999. Our graduates all gained important positions in large and medium-sized companies around the world (from Brussels over London & Toronto to Singapore and Shanghai). Most companies especially value the many practical skills our students acquired during the many real-life projects they performed during their study of the Master of Marketing Analysis) The admission procedure is very strict both in terms of quality and procedure (submission deadline for international students is February 1st). The long-term acceptance rate amounts to about 4 %.

 

§        Master of Statistical Data Analysis (in English since in October 2006)

This one-year full-time program is available in English since October 2006. Several data-mining courses are offered as elective courses in this degree.

 

 

 

Research

 

Data-Mining Papers about Analytical Customer Relationship Management (CRM) / Business Intelligence in Marketing / Customer Intelligence in Marketing

 

Click on paper titles to obtain the full electronic version (then continue by clicking on the “Downloads” link)

 

Visit our new website about our Text Mining for Customer Intelligence website.

 

n      Random Forests

n      NEW – PRINZIE Anita & VAN DEN POEL Dirk (2008), Random Forests for Multiclass classification: Random Multinomial Logit, Expert Systems with Applications, 34 (3), 1721-1732.

n      PRINZIE Anita & VAN DEN POEL Dirk (2006), Exploiting Randomness for Feature Selection in Multinomial Logit: A CRM Cross-Sell Application, Lecture Notes in Artificial Intelligence, 4065, 310-323.

n      BUCKINX W., VERSTRAETEN G., VAN DEN POEL D. (2007), Predicting Customer Loyalty Using the Internal Transactional Database, Expert Systems with Applications, 32 (1), 125-134.

n      LARIVIERE B., VAN DEN POEL D. (2005), Predicting Customer Retention and Profitability by Using Random Forest and Regression Forest Techniques, Expert Systems with Applications, 29 (2), 472-484.

n      BUCKINX Wouter, VAN DEN POEL Dirk (2005), “Customer Base Analysis: Partial Defection of Behaviorally-Loyal Clients in a Non-Contractual FMCG Retail Setting”, European Journal of Operational Research, 164 (1), 252-268.

 

n      Support Vector Machines

n      COUSSEMENT Kristof, VAN DEN POEL Dirk (2008), Churn Prediction in Subscription Services: An Application of Support Vector Machines While Comparing Two Parameter-Selection Techniques, Expert Systems with Applications, 34 (1), 313-327.

 

 

n      Neural Networks

n      ARD (Automatic Relevance Determination)

n      NEW - BUCKINX W., VERSTRAETEN G., VAN DEN POEL D. (2007), Predicting Customer Loyalty Using the Internal Transactional Database, Expert Systems with Applications, 32 (1), 125-134.

n      BUCKINX Wouter, VAN DEN POEL Dirk (2005), “Customer Base Analysis: Partial Defection of Behaviorally-Loyal Clients in a Non-Contractual FMCG Retail Setting”, European Journal of Operational Research, 164 (1), 252-268.

n      BAESENS Bart, VIAENE Stijn, VAN DEN POEL Dirk, VANTHIENEN Jan, DEDENE Guido (2002), “Bayesian Neural Network Learning for Repeat Purchase Modelling in Direct Marketing”, European Journal of Operational Research, 138 (1), 191-211.

n      Other Neural Networks

n      VAN GESTEL T., BAESENS B., SUYKENS J.A.K., VAN DEN POEL D., BAESTAENS D-E., WILLEKENS M. (2006), Bayesian Kernel-Based Classification for Financial Distress Detection, European Journal of Operational Research (EJOR), 172 (3), 979-1003.

 

n      Bayesian Networks

n      BAESENS Bart, VERSTRAETEN Geert, VAN DEN POEL Dirk, EGMONT-PETERSEN M., VAN KENHOVE P., VANTHIENEN J. (2004), “Bayesian Network Classifiers for Identifying the Slope of the Customer-Lifecycle of Long-Life Customers”, European Journal of Operational Research, 156 (2), 508-523, 2004.

 

n      Sequence Alignment Method

n      PRINZIE Anita, VAN DEN POEL Dirk (2006), “Incorporating sequential information into traditional classification models by using an element/position-sensitive SAM”, Decision Support Systems, 42 (2), 508-526.

 

n      New algorithms

n      NEW – PRINZIE Anita & VAN DEN POEL Dirk (2008), Random Forests for Multiclass classification: Random Multinomial Logit, Expert Systems with Applications, 34 (3), 1721-1732.

n      BUREZ Jonathan, VAN DEN POEL Dirk (2007), CRM at a Pay-TV Company: Using Analytical Models to Reduce Customer Attrition by Targeted Marketing for Subscription Services, Expert Systems with Applications, 32 (2), 277-288.

n      PRINZIE Anita & VAN DEN POEL Dirk (2005), Constrained optimization of data-mining problems to improve model performance: A direct-marketing application, Expert Systems with Applications, 29 (3), 630-640.

n      JONKER J.J., PIERSMA N. & VAN DEN POEL D. (2004), “Joint Optimization of Customer Segmentation and Marketing Policy to Maximize Long-Term Profitability”, Expert Systems with Applications, 27 (2), 159-168.

 

n      Feature Selection

n      NEW – PRINZIE Anita & VAN DEN POEL Dirk (2008), Random Forests for Multiclass classification: Random Multinomial Logit, Expert Systems with Applications, 34 (3), 1721-1732.

n      NEW – BUCKINX W., VAN DEN POEL D. (2008), Assessing and exploiting the profit function by modeling the net impact of targeted marketing, European Journal of Operational Research, Forthcoming.

n      PRINZIE Anita & VAN DEN POEL Dirk (2007), Random Multiclass Classification: Generalizing Random Forests to Random MNL and Random NB, Lecture Notes in Computer Science, LNCS 4653, 349-358.

n      NEW – PRINZIE Anita & VAN DEN POEL Dirk (2007), Predicting home-appliance acquisition sequences: Markov/MTD/MTDg and survival analysis for modeling sequential information in NPTB models, Decision Support Systems, 44 (1), 28-45.

n      BUCKINX W., VERSTRAETEN G., VAN DEN POEL D. (2007), Predicting Customer Loyalty Using the Internal Transactional Database, Expert Systems with Applications, 32 (1), 125-134.

n      VAN DEN POEL Dirk, BUCKINX Wouter (2005), “Predicting Online-Purchasing Behavior”, European Journal of Operational Research, 166 (2), 2005, 557-575.

n      BUCKINX Wouter et al. (2004), “Customer-Adapted Coupon Targeting Using Feature Selection”, Expert Systems with Applications, 26 (4), 2004, 509-518.

n      VAN DEN POEL Dirk, “Predicting Mail-Order Repeat Buying: Which Variables Matter?”, Tijdschrift voor Economie & Management, 48 (3), 371-403.

 

n      Market Basket Analysis

n      VINDEVOGEL B., VAN DEN POEL D., WETS G. (2005), Why promotion strategies based on market basket analysis do not work, Expert Systems with Applications, 28 (3), 583-590.

n      VAN DEN POEL Dirk et al. (2004), “Direct and Indirect Effects of Retail Promotions”, Expert Systems with Applications, 27 (1), 53-62.

 

n      Survival Analysis (Statistical & Data Mining)

n      NEW – BUREZ Jonathan, VAN DEN POEL Dirk (2009), Separating Financial From Commercial Customer Churn: A Modeling Step Towards Resolving The Conflict Between The Sales And Credit Department, Expert Systems with Applications, Forthcoming.

n      NEW – PRINZIE Anita & VAN DEN POEL Dirk (2007), Predicting home-appliance acquisition sequences: Markov/MTD/MTDg and survival analysis for modeling sequential information in NPTB models, Decision Support Systems, 44 (1), 28-45.

n      BAESENS B., VAN GESTEL T., STEPANOVA M., VAN DEN POEL D. (2005), Neural Network Survival Analysis for Personal Loan Data, Journal of the Operational Research Society (JORS), 56 (9), 1089-1098.

n      LARIVIERE Bart & VAN DEN POEL Dirk (2004), "Investigating the role of product features in preventing customer churn,by using survival analysis and choice modeling: The case of financial services", Expert Systems with Applications, 27 (2), 277-285.

n      LARIVIΘRE B. & VAN DEN POEL D. (2005), Investigating the post-complaint period by means of survival analysis, Expert Systems with Applications, 29 (3), 667-677.

n      VAN DEN POEL Dirk, LARIVIΘRE Bart (2004), “Customer Attrition Analysis for Financial Services Using Proportional Hazard Models”, European Journal of Operational Research, 157 (1), 196-217.

 

n      MTD, MTDg models

n      PRINZIE Anita & VAN DEN POEL Dirk (2006), “Investigating Purchasing Patterns for Financial Services using Markov, MTD and MTDg Models”, European Journal of Operational Research, 170 (3), 710-734.

 

 

Link to interesting data mining material: KDnuggets

 

This page is maintained by Prof. Dr. Dirk Van den Poel, Department of Marketing at Ghent University, Belgium