Cluster analysis in industrial market segmentation through artificial neural network

Authors:
R. J. Kuo;L. M. Ho;C. M. Hu
Affiliations:
Department of Industrial Engineering, National Taipei University of Technology, 1 Section 3, Chung-Hsiao East Road, Taipei 106, Taiwan, ROC;Department of Leisure, Recreation and Tourism Management, Shu-Te University, Kaohsiung County 824, Taiwan, ROC;Division of System Information, Foxconn Industrial PCE Production Group, Hsing-Chu, Taiwan, ROC
Venue:
Computers and Industrial Engineering - 26th International conference on computers and industrial engineering
Year:
2002

Citing 5
Cited 6

Introduction to the theory of neural computation

Introduction to the theory of neural computation
Data mining with neural networks: solving business problems from application development to decision support

Data mining with neural networks: solving business problems from application development to decision support
Intelligent decision support methods: the science of knowledge work

Intelligent decision support methods: the science of knowledge work
A comparative study of self-organizing clustering algorithms dignet and ART2

Neural Networks
Intelligent Systems for Finance and Business

Intelligent Systems for Finance and Business

Employing data mining to identify the significant rules for classifying body types

CEA'07 Proceedings of the 2007 annual Conference on International Conference on Computer Engineering and Applications
Data mining to improve industrial standards and enhance production and marketing: An empirical study in apparel industry

Expert Systems with Applications: An International Journal
A neural network classifier with rough set-based feature selection to classify multiclass IC package products

Advanced Engineering Informatics
Using data mining to extract sizing knowledge for promoting manufacture

ACS'06 Proceedings of the 6th WSEAS international conference on Applied computer science
Optimal product positioning with consideration of negative utility effect on consumer choice rule

Decision Support Systems
Review: Soft computing applications in customer segmentation: State-of-art review and critique

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Market segmentation has commonly applied cluster analysis. This study intends to make the comparison of conventional two-stage method with proposed two-stage method through the simulated data. The proposed two-stage method is the combination of self-organizing feature maps and K-means method. The simulation results show that the proposed scheme is better than the conventional two-stage method based on the rate of misclassification.