Harish Verma, Eatesh Kandpal, Bipul Pandey and Joydip Dhar (2010) A Novel Document Clustering Algorithm Using Squared Distance Optimization Through Genetic Algorithms

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A Novel Document Clustering Algorithm Using Squared Distance Optimization Through Genetic Algorithms

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CONTRIBUTORS:

	Author:	Harish Verma
	Author:	Eatesh Kandpal
	Author:	Bipul Pandey
	Author:	Joydip Dhar

JOURNAL:

International Journal on Computer Science and Engineering (IJCSE), 2(5), 1875 - 1879.

YEAR:

2010

PUB TYPE:

Journal Article

SUBJECT(S):

Genetic algorithm; optimization; Document clustering; k-means; mutation; crossover.

DISCIPLINE:

Computer Science

HTTP:

http://www.enggjournals.com/ijcse/doc/IJCSE10-02-05-49.pdf

LANGUAGE:

English

PUB ID:

103-488-666 (Last edited on 2011/06/12 03:20:01 GMT-6)

SPONSOR(S):

ABSTRACT:
K-Means Algorithm is most widely used algorithms in document clustering. However, it still suffer some shortcomings like random initialization, solution converges to local minima, and empty cluster formation. Genetic algorithm is often used for document clustering because of its global search and optimization ability over heuristic problems. In this paper, search ability of genetic algorithm has exploited with a modification from the general genetic algorithm by not using the random initial population.A new algorithm for population initialization is given in this paper and results are compared with k-means algorithm.

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