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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/17619

Title: A quantum evolutionary algorithm for data clustering
Authors: Ramdane, Chafika.
Meshoul, Souham.
Batouche, Mohamed.
-Khireddine Kholladi, Mohamed.
تاريخ النشر: 2010
Publisher: International Journal of Data Mining, Modelling and Management (IJDMMM), Vol. 2, No. 4, 2010IJDMMM), Vol. 2, No. 4, 2010
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.
URI: http://hdl.handle.net/123456789/17619
يظهر في المجموعات:College of Computer and Information Sciences

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