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tribecagamosisAI and Robotics

Nov 8, 2013 (3 years and 9 months ago)

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Publication #8




Title

A quantum evolutionary algorithm for data clustering

Author
-
s


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

Contact lnfo

smeshoul@ksu.edu.sa

Department

Information Technology

Major

Information Technology

citation


Year of
Publication

2010

Publisher

International Journal of Data Mining, Modelling

and Management (IJDMMM), Vol.
2, No. 4, 2010IJDMMM), Vol. 2, No. 4, 2010

Sponsor

King Saud University

Type of
Publication

Conference Proceedings

ISSN


URI/DOI


Full Text (Yes,
No)

No

Key words


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 pr
oblem. 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 reli
es 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 abili
ty of the method to identify valid clusters and also its
effectiveness comparing to other evolutionary algorithms.