In search of optimal clusters using genetic algorithms
Pattern Recognition Letters
An evolutionary technique based on K-means algorithm for optimal clustering in RN
Information Sciences—Applications: An International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Mining tourist imagery to construct destination image position model
Expert Systems with Applications: An International Journal
A hybrid approach for supplier cluster analysis
Computers & Mathematics with Applications
Using the Taguchi method for effective market segmentation
Expert Systems with Applications: An International Journal
Marketing Positioning System Designing Based on Extenics
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Hi-index | 12.05 |
Marketing segmentation is widely used for targeting a smaller market and is useful for decision makers to reach all customers effectively with one basic marketing mix. Although clustering algorithms is popularly employed in dealing with this problem, it cannot be useful unless irrelevant variables are removed because irrelevant variables will distort the clustering structure and make the results useless. In this paper, genetic algorithms (GA) is used for variable selection and for determining the numbers of clusters. A real case of bank data set is used for illustrating the application of marketing segmentation. The results show that variable selection through GA can effectively find the global optimum solution, and the accuracy of the classified model is dramatically increased after clustering.