Mining Frequent Item Sets from incremental database: A single pass approach

Sandhya Rani Jetti; Dr.Sujatha D

Mining Frequent Item Sets from incremental database: A single pass approach

Sandhya Rani Jetti * §

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Dr.Sujatha D

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Mining Frequent Item Sets from incremental database: A single pass approach

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Abstract

Apriori based Association Rule Mining (ARM) is one of the data mining techniques used to extract hidden knowledge from datasets that can be used by an organization’s decision makers to improve overall profit. Performing Existing association mining algorithms requires repeated passes over the entire database. Obviously, for large database, the role of input/output overhead in scanning the database is very significant. We propose a new algorithm, which would mine frequent item sets with vertical format. The new algorithm would need to scan database one time. And in the follow-up data mining process, it can get new frequent item sets through ‘and operation’ between item sets. The new algorithm needs less storage space, and can improve the efficiency of data mining.

References

4 Cites in Article

Reference Format

Margaret Dunham (2005). Data Mining Itroductory and Advanced Topics.
Jiawei Han,Micheline Kamber (2006). Data Mining Concepts and Techniques.
Wang Cuiru,Wang Shaohua (2008). An Improved Apriori Algorithm for Association Rules.
Hao Hu,Kejie Zhai,Niannian Wang,Bin Li,Xueming Du,Kangjian Yang (2007). Failure Analysis of Damaged Prestressed Concrete Cylinder Pipes Strengthened with Prestressed Carbon Fiber-Reinforced Polymers.

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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

Sandhya Rani Jetti, Dr.Sujatha D. 1970. "Mining Frequent Item Sets from incremental database: A single pass approach". Global Journal of Computer Science and Technology GJCST Volume 11 (GJCST Volume 11 Issue 21).

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GJCST Volume 11 Issue 21

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

Apriori Association rule Data mining vertical format

Classification

GJCST Classification H.2.8

Submission ReceivedOctober 20, 2011
Peer Review Double Blind
Handling Editor
Accepted November 14, 2011
Published November 28, 2011

Version of record

v1.2

Issue date

December 27, 2011

Language

English

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Article Matrices

Total Score: 147

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Dr.Sujatha D,Sandhya Rani Jetti (PhD/Dr. count: 1)

View Count (all-time): 363

Total Views (Real + Logic): 4985

Total Downloads (simulated): 385

Publish Date: 2011 11, Mon

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Total Views: 5K

Total Downloads: 385

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