Genetic algorithm for item selection with cross-selling considerations

Author

Cheng, Yan

Author_Institution

Inf. Manage. & Inf. Syst. Dept., Fudan Univ., Shanghai, China

Volume

6

fYear

2005

fDate

18-21 Aug. 2005

Firstpage

3293

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on

Conference_Location

Guangzhou, China

Print_ISBN

0-7803-9091-1

Type

conf

DOI

10.1109/ICMLC.2005.1527511

Filename

1527511