Data Mining in Marketing Using Bayesian Networks and Evolutionary Programming

Give the explosive growth of customer data collected electronically from current electronic business environment, data mining can potentially discover new knowledge to improve managerial decision making in marketing. This study proposes an innovative approach to data mining using Bayesian Networks and evolutionary programming and applies the methods to marketing data. The results suggest that this approach to knowledge discovery can generate superior results than the conventional method of logistic regression. Future research in this area should devote more attention to applying this and other data mining methods to solving complex problems facing today’s electronic businesses.

[1]  Nissan Levin,et al.  Predictive modeling using segmentation , 2001 .

[2]  David B. Fogel,et al.  An introduction to simulated evolutionary optimization , 1994, IEEE Trans. Neural Networks.

[3]  Bruce D'Ambrosio,et al.  Inference in Bayesian Networks , 1999, AI Mag..

[4]  Finn Verner Jensen,et al.  Introduction to Bayesian Networks , 2008, Innovations in Bayesian Networks.

[5]  Timothy L. Urban An inventory-theoretic approach to product assortment and shelf-space allocation , 1998 .

[6]  Siddhartha Bhattacharyya,et al.  Evolutionary algorithms in data mining: multi-objective performance modeling for direct marketing , 2000, KDD '00.

[7]  Wai Lam,et al.  Bayesian Network Refinement Via Machine Learning Approach , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Varghese S. Jacob,et al.  Genetic Algorithms for Product Design , 1996 .

[9]  Michael P. Wellman,et al.  Bayesian networks , 1995, CACM.

[10]  David Heckerman,et al.  Knowledge Representation and Inference in Similarity Networks and Bayesian Multinets , 1996, Artif. Intell..

[11]  B. R. Klemz Using genetic algorithms to assess the impact of pricing activity timing , 1999 .

[12]  J.C.Y. Cheng,et al.  Discovering knowledge from medical databases using evolutionory algorithms , 2000, IEEE Engineering in Medicine and Biology Magazine.

[13]  Wai Lam,et al.  LEARNING BAYESIAN BELIEF NETWORKS: AN APPROACH BASED ON THE MDL PRINCIPLE , 1994, Comput. Intell..

[14]  P. Spirtes,et al.  Causation, prediction, and search , 1993 .

[15]  A. Silk Marketing Science in a Changing Environment , 1993 .

[16]  Peter Haddawy,et al.  An Overview of Some Recent Developments in Bayesian Problem-Solving Techniques , 1999, AI Mag..

[17]  Pedro Larrañaga,et al.  Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Kwong-Sak Leung,et al.  Medical Data Mining Using Evolutionary Computation , 2022 .

[19]  Judea Pearl,et al.  The recovery of causal poly-trees from statistical data , 1987, Int. J. Approx. Reason..

[20]  D. Midgley,et al.  Breeding competitive strategies , 1997 .

[21]  Luiz Moutinho,et al.  Solving marketing optimization problems using genetic algorithms , 1995 .

[22]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[23]  Kwong-Sak Leung,et al.  Using Evolutionary Programming and Minimum Description Length Principle for Data Mining of Bayesian Networks , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Gregory F. Cooper,et al.  A Bayesian method for the induction of probabilistic networks from data , 1992, Machine Learning.