An Effcient Algorithm for Mining Association Rules In Massive Datasets

Dr. D. Gunaseelan; P. Uma

An Effcient Algorithm for Mining Association Rules In Massive Datasets

Dr. D. Gunaseelan * §

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P. Uma

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§ JAZAN University, Kingdom of Saudi Arabia

An Effcient Algorithm for Mining Association Rules In Massive Datasets

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Abstract

Data mining, also known as Knowledge Discovery in Databases (KDD) is one of the most important and interesting research areas in 21st century. Frequent pattern discovery is one of the important techniques in data mining. The application includes Medicine, Telecommunications and World Wide Web. Nowadays frequent pattern discovery research focuses on finding co-occurrence relationships between items. Apriori algorithm is a classical algorithm for association rule mining. Lots of algorithms for mining association rules and their mutations are proposed on the basis of Apriori algorithm. Most of the previous algorithms Apriori-like algorithm which generates candidates and improving algorithm strategy and structure but at the same time many of the researchers not concentrate on the structure of database. In this research paper, it has been proposed an improved algorithm for mining frequent patterns in large datasets using transposition of the database with minor modification of the Apriori-like algorithm. The main advantage of the proposed method is the database stores in transposed form and in each iteration database is filtered and reduced by generating the transaction id for each pattern. The proposed method reduces the huge computing time and also decreases the database size. Several experiments on real-life data show that the proposed algorithm is very much faster than existing Apriori-like algorithms. Hence the proposed method is very much suitable for the discovering frequent patterns from large datasets.

References

10 Cites in Article

Reference Format

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Dr. Gunaseelan, P. Uma. 2012. "An Effcient Algorithm for Mining Association Rules In Massive Datasets". Global Journal of Computer Science and Technology, Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 12 (GJCST Volume 12 Issue C13).

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GJCST Volume 12 Issue C13

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Apriori Algorithm Data mining frequent pattern mining transposition of database

Classification

GJCST-C Classification H.2.8

Submission ReceivedDecember 12, 2011
Peer Review Double Blind
Handling Editor
Accepted January 4, 2012
Published January 15, 2012

Version of record

v1.2

Issue date

September 6, 2012

Language

English

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Article Matrices

Total Score: 167

Country: Saudi Arabia

Subject: Global Journal of Computer Science and Technology

Authors: Dr. D. Gunaseelan, P. Uma (PhD/Dr. count: 1)

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Publish Date: 2012 01, Sun

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