Title: A quantum evolutionary algorithm for data clustering

Authors: Chafika Ramdane, Souham Meshoul, Mohamed Batouche, Mohamed-Khireddine Kholladi

Addresses: Computer Science Department, University of Skikda, El-Hadaiek Road PB 26, Skikda, 21000, Algeria. ' College of Computer and Information Sciences, Center of Excellence in Information Assurance, P.O. Box 51178, Riyadh 11543, Saudi Arabia. ' College of Computer and Information Sciences, Center of Excellence in Information Assurance, P.O. Box 51178, Riyadh 11543, Saudi Arabia. ' Computer Science Department, University of Constantine, MISC Laboratory, PB 325, Ain El Bey Road, Constantine 25017, Algeria

Abstract: The emerging field of quantum computing has recently created much interest in the computer science community due to the new concepts it suggests to store and process data. In this paper, we explore some of these concepts to cope with the data clustering problem. Data clustering is a key task for most fields like data mining and pattern recognition. It aims to discover cohesive groups in large datasets. In our work, we cast this problem as an optimisation process and we describe a novel framework, which relies on a quantum representation to encode the search space and a quantum evolutionary search strategy to optimise a quality measure in quest of a good partitioning of the dataset. Results on both synthetic and real data are very promising and show the ability of the method to identify valid clusters and also its effectiveness comparing to other evolutionary algorithms.

Keywords: data clustering; evolutionary algorithms; quantum computing; quantum representation; optimisation; data mining; dataset partitioning.

DOI: 10.1504/IJDMMM.2010.035564

International Journal of Data Mining, Modelling and Management, 2010 Vol.2 No.4, pp.369 - 387

Published online: 30 Sep 2010 *

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