Integration of Particle Swarm Optimization and Immune Genetic Algorithm-Based Dynamic Clustering for Customer Clustering-Reference-Cited by-同舟云学术

Integration of Particle Swarm Optimization and Immune Genetic Algorithm-Based Dynamic Clustering for Customer Clustering

Published:2015-10 Issue:05 Volume:24 Page:1550019
ISSN:0218-2130
Container-title:International Journal on Artificial Intelligence Tools
language:en
Short-container-title:Int. J. Artif. Intell. Tools

Author:

Kuo R. J.¹,Lin S. H.²,Chen Zhen-Yao³

Affiliation:

1. Department of Industrial Management, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Road, Taipei City 10607, Taiwan, R.O.C.

2. Taiwan Semiconductor Manufacturing Company, No. 8, Li-Hsin Road, 6, Hsinchu Science Park, Hsinchu City 30078, Taiwan, R.O.C.

3. Department of Business Administration, De Lin Institute of Technology, No. 1, Ln. 380, Qingyun Road, Tucheng Dist, New Taipei City 23654, Taiwan, R.O.C.

Abstract

This study intends to present a dynamic clustering (DC) approach based on particle swarm optimization (PSO) and immune genetic (IG) (DCPIG) algorithm, which is able to cluster the data into adequate clusters through data characteristics with pre-specified numbers of clusters. The proposed DCPIG algorithm is compared with three DC algorithms in the literature using Iris, Wine, Glass and Vowel benchmark data sets. The experiment results show that the DCPIG algorithm can achieve higher stability and accuracy than the other algorithms. In addition, the DCPIG algorithm is also applied to a real-world problem considering the customer clustering for a cyber flower shop. Lastly, we recommend different products and services to customers based on the clustering results.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Artificial Intelligence

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0218213015500190

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