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A Quantum-inspired Genetic Algorithm for Data Clustering

Authors
Xiao, JingYan, YuPingLin, YingYuan, LingZhang, Jun
Issue Date
Jun-2008
Publisher
IEEE
Citation
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp 1513 - 1519
Pages
7
Indexed
SCIE
SCOPUS
Journal Title
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)
Start Page
1513
End Page
1519
URI
https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/115972
DOI
10.1109/CEC.2008.4630993
Abstract
The conventional K-Means clustering algorithm must know the number of clusters in advance and the clustering result is sensitive to the selection of the initial cluster centroids. The sensitivity may make the algorithm converge to the local optima. This paper proposes an improved K-Means clustering algorithm based on Quantum-inspired Genetic Algorithm (KMQGA). In KMQGA, Q-bit based representation is employed for exploration and exploitation in discrete 0-1 hyperspace by using rotation operation of quantum gate as well as three genetic algorithm operations (Selection, Crossover and Mutation) of Q-bit. Without knowing the exact number of clusters beforehand, the KMQGA can get the optimal number of clusters as well as providing the optimal cluster centroids after several iterations of the four operations (Selection, Crossover, Mutation, and Rotation). The simulated datasets and the real datasets are used to validate KMQGA and to compare KMQGA with an improved K-Means clustering algorithm based on the famous Variable string length Genetic Algorithm (KMVGA) respectively. The experimental results show that KMQGA is promising and the effectiveness and the search quality of KMQGA is better than those or KMVGA.
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ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)
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