A quantum evolutionary algorithm for data clustering

Chafika Ramdane, Souham Meshoul, Mohamed Batouche and Mohamed-Khireddine Kholladi

International Journal of Data Mining, Modelling and Management, 2010, vol. 2, issue 4, 369-387

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. (search for similar items in EconPapers)
Date: 2010
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=35564 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijdmmm:v:2:y:2010:i:4:p:369-387

Access Statistics for this article

More articles in International Journal of Data Mining, Modelling and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().