TY - JOUR
T1 - Using a clustering genetic algorithm to support customer segmentation for personalized recommender systems
AU - Kim, Kyoung Jae
AU - Ahn, Hyunchul
PY - 2005
Y1 - 2005
N2 - This study proposes novel clustering algorithm based on genetic algorithms (GAs) to carry out a segmentation of the online shopping market effectively. In general, GAs are believed to be effective on NP-complete global optimization problems and they can provide good sub-optimal solutions in reasonable time. Thus, we believe that a clustering technique with GA can provide a way of finding the relevant clusters. This paper applies GA-based K-means clustering to the real-world online shopping market segmentation case for personalized recommender systems. In this study, we compare the results of GA-based K-means to those of traditional K-means algorithm and self-organizing maps. The result shows that GA-based K-means clustering may improve segmentation performance in comparison to other typical clustering algorithms.
AB - This study proposes novel clustering algorithm based on genetic algorithms (GAs) to carry out a segmentation of the online shopping market effectively. In general, GAs are believed to be effective on NP-complete global optimization problems and they can provide good sub-optimal solutions in reasonable time. Thus, we believe that a clustering technique with GA can provide a way of finding the relevant clusters. This paper applies GA-based K-means clustering to the real-world online shopping market segmentation case for personalized recommender systems. In this study, we compare the results of GA-based K-means to those of traditional K-means algorithm and self-organizing maps. The result shows that GA-based K-means clustering may improve segmentation performance in comparison to other typical clustering algorithms.
UR - http://www.scopus.com/inward/record.url?scp=26844514586&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-30583-5_44
DO - 10.1007/978-3-540-30583-5_44
M3 - Conference article
AN - SCOPUS:26844514586
SN - 0302-9743
VL - 3397
SP - 409
EP - 415
JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 13th International Conference on AIS 2004
Y2 - 4 October 2004 through 6 October 2004
ER -