Using a clustering genetic algorithm to support customer segmentation for personalized recommender systems

Kyoung Jae Kim, Hyunchul Ahn

Research output: Contribution to journalConference articlepeer-review

29 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)409-415
Number of pages7
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3397
DOIs
StatePublished - 2005
Event13th International Conference on AIS 2004 - Jeju Island, Korea, Republic of
Duration: 4 Oct 20046 Oct 2004

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