Quantum-inspired evolutionary clustering algorithm based on manifold distance

Authors:
Yangyang Li;Hongzhu Shi;Maoguo Gong;Ronghua Shang
Affiliations:
Xidian University, Xi'an, China;Xidian University, Xi'an, China;Xidian University, Xi'an, China;Xidian University, Xi'an, China
Venue:
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Year:
2009

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Abstract

Based on the concepts and principles of quantum computing, a quantum-inspired evolutionary algorithm for data clustering (QECA) is proposed in this paper. And a novel distance measurement index called manifold distance is introduced. These attribute data are the main source of clustering problem, due to its complex distribution, most clustering algorithms available are only suitable for these types of characteristic data. In this study, a new algorithm which can deal with these data with manifold distribution is more effective. The main motives of using QECA consist in searching for appropriate cluster center so that a similarity metric of clusters are optimized more quickly and effectively. The superiority of QECA over fuzzy c-means (FCM) algorithm and immune evolutionary clustering algorithm (IECA) is extensively demonstrated in our experiments.