Title :
Genetic algorithm for item selection with cross-selling considerations
Author_Institution :
Inf. Manage. & Inf. Syst. Dept., Fudan Univ., Shanghai, China
Abstract :
A fundamental problem in chain convenience store is selecting items with respect to notion of "cross-selling effect". Recent works have proven the problem is NP-hard. In this paper, genetic algorithm is applied to this problem. Based on the features of genetic algorithm, a method of defining imprecise fitness function is proposed which is suited for solving item selection with imprecision cross-selling effect. Quantitative analysis method on "cross-selling effect" is also presented in this paper, which employs the ideas of quantitative association rules, a recent data mining technique to discover quantitative affinities in large transaction databases.
Keywords :
data mining; genetic algorithms; retail data processing; very large databases; NP-hard problem; cross-selling effect; data mining technique; fitness function; genetic algorithm; large transaction databases; quantitative analysis method; Association rules; Consumer electronics; Data mining; Decision making; Genetic algorithms; Heart; Information management; Management information systems; Marketing and sales; Transaction databases; Cross-selling; Data Mining; Genetic Algorithm; Quantitative Association Rule;
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
DOI :
10.1109/ICMLC.2005.1527511
