- author = "A. E. Eiben and T. J. Euverman and W. Kowalczyk and F. Slisser",
- title = "Modelling Customer Retention with Statistical Techniques, Rough Data Models and Genetic Programming",
- booktitle = "Rough-Fuzzy Hybridization: A New Trend in Decision Making Fuzzy Sets, Rough Sets and Decision Making Processes",
- publisher = "Springer-Verlag",
- year = "1998",
- editor = "Sankar K. Pal and Andrzej Skowron",
- pages = "330--345",
- address = "Berlin",
- keywords = "genetic algorithms, genetic programming",
- ISBN = "981-4021-00-8",
-
URL = "
http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=DF680800F7770919CB85C7A704F50DC9?doi=10.1.1.55.7177&rep=rep1&type=pdf",
- size = "16 pages",
- abstract = "This paper contains results of a research project aiming at modelling the phenomenon of customer retention. Historical data from a database of a big mutual fund investment company have been analysed with three techniques: logistic regression, rough data models, and genetic programming. Models created by these techniques were used to gain insights into factors influencing customer behaviour and to make predictions on ending the relationship with the company in question. Because the techniques were applied independently of each other, it was possible to make a comparison of their basic features in the context of data mining.",
-
notes = "
http://www.amazon.com/Rough-Fuzzy-Hybridization-Decision-Making/dp/9814021008",
