BibTeX
@MISC{Wong_decision,
author = {Man Leung Wong},
title = {Decision},
year = {}
}
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Abstract
Given the explosive growth of data collected from current business environment, data mining can potentially discover new knowledge to improve managerial decision making. We propose a novel data mining approach that employs evolutionary programming to discover knowledge represented in Bayesian networks and apply the approach to marketing data. There are two different approaches to the network learning problem. The first one uses dependency analysis, while the second approach searches good network structures according to a metric. Unfortunately, the two approaches both have their own drawbacks. Thus, we propose a novel hybrid of the two approaches. With this new idea, we endeavor to improve upon our previous work, MDLEP, which uses evolutionary programming for network learning. We also introduce a new operator to further enhance the search efficiency. We conduct a number of experiments and compare the hybrid approach with MDLEP. The empirical results illustrate that the approach improves over MDLEP. 1
Keyphrases
evolutionary programming previous work dependency analysis marketing data data mining novel hybrid empirical result network learning search efficiency hybrid approach bayesian network new knowledge novel data mining approach different approach new operator managerial decision making new idea current business environment explosive growth
