Data Mining at UGent
Education
Master of Science in Marketing Analysis
This 9-month full-time program in predictive analytics (from October to July) is taught 100 % 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 EUR = 900 USD (i.e., the regular full-year admission fee for all students to all university degrees in Belgium) + approx. 200 EUR = 260 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 %. Click here for more information.
Master of Science in Business Engineering: elective Marketing Engineering
This two-year full-time program in business analytics is partially taught in English. Click here for more information.
Master of Science in Statistical Data Analysis
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. Click here for more information.
Research
Random Forests
BALLINGS M. & VAN DEN POEL D. (2013), Kernel Factory: An ensemble of kernel machines, Expert Systems with Applications, 40 (8), 2904-2913.
DE BOCK K.W. & VAN DEN POEL D. (2012), Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models, Expert Systems with Applications, 39 (8), 6816-6826.
DE BOCK K.W. & VAN DEN POEL D. (2011), An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction, Expert Systems with Applications, 38 (10), 12293-12301.
BAECKE Ph & VAN DEN POEL D. (2011), Data Augmentation by Predicting Spending Pleasure Using Commercially Available External Data, Journal of Intelligent Information Systems, 36 (3), 367-383.
PRINZIE Anita & VAN DEN POEL Dirk (2008), Random Forests for Multiclass classification: Random Multinomial Logit, Expert Systems with Applications, 34 (3), 1721-1732.
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.
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, LNCS 4065, 310-323.
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.
Support Vector Machines
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.
Neural Networks
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.
Sequence Analysis
V.L. Migueis, Dirk Van den Poel, A.S. Camanho, Joao Falcao e Cunha (2012), Predicting partial customer churn using Markov for Discrimination for modeling rst purchase sequences, Forthcoming in Advances in Data Analysis and Classification.
V.L. Migueis, Dirk Van den Poel, A.S. Camanho, Joao Falcao e Cunha (2012), Modeling partial customer churn: On the value of first product-category purchase sequences, Expert Systems with Applications, 39 (12), 11250-11256.
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.
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.
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.
Market Basket Analysis
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.
VAN DEN POEL Dirk et al. (2004), Direct and Indirect Effects of Retail Promotions, Expert Systems with Applications, 27 (1), 53-62.
Social Network Analysis (SNA)
BENOIT D.F. & VAN DEN POEL D. (2012), Improving Customer Retention in Financial Services Using Kinship Network Information, Expert Systems with Applications, 39 (13), 11435-11442.
Quantile Regression
V.L. Migueis, D. Benoit, Dirk Van den Poel, A.S. Camanho, Joao Falcao e Cunha (2012), Enhanced Decision Support in Credit Scoring Using Bayesian Binary Quantile Regression, Forthcoming in Journal of the Operational Research Society.
BENOIT D. & VAN DEN POEL D. (2012), Binary Quantile Regression: A Bayesian Approach based on the Asymmetric Laplace Density, Journal of Applied Econometrics, 27 (7), 12105-12113.
BENOIT D.F., VAN DEN POEL D. (2009), Benefits of quantile regression for the analysis of customer lifetime value in a contractual setting: An application in financial services, Expert Systems with Applications, 36 (7), 10475-10484.
Survival Analysis
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.
VAN DEN POEL Dirk, LARIVIERE Bart (2004), Customer Attrition Analysis for Financial Services Using Proportional Hazard Models, European Journal of Operational Research, 157 (1), 196-217.