V.Umarani and Dr.M.Punithavalli (2010) Sliding window approach based Text Binarisation from Complex Textual images

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Sliding window approach based Text Binarisation from Complex Textual images

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CONTRIBUTORS:

	Author:	V.Umarani
	Author:	Dr.M.Punithavalli

JOURNAL:

International Journal on Computer Science and Engineering (IJCSE), 02(02), 314 - 318.

YEAR:

2010

PUB TYPE:

Journal Article

SUBJECT(S):

Datamining, sampling, Association rule mining, data reduction technique, Frequent pattern.

DISCIPLINE:

Computer Science

HTTP:

http://www.enggjournals.com/ijcse/doc/IJCSE10-02-02-37.pdf

LANGUAGE:

English

PUB ID:

103-470-521 (Last edited on 2010/04/07 01:35:59 GMT-6)

SPONSOR(S):

ABSTRACT:
Association rule discovery from large databases is one of the tedious tasks in datamining.The process of frequent itemset mining, the first step in the mining of association rules, is a computational and IO intensive process necessitating repeated passes over the entire database. Sampling has been often suggested as an effective tool to reduce the size of the dataset operated at some cost to accuracy. Data mining literature presents with numerous sampling based approaches to speed up the process of Association Rule Mining(ARM).Sampling is one of the important and popular data reduction technique that is used to mine huge volume of data efficiently. Sampling can speed up the mining of association rules. In this paper, we provide an overview of existing sampling based association rule mining algorithms.

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