Various Frequent Item Set on Data Mining Technique

Various Frequent Item Set on Data Mining Technique

Manisha Kundal

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Dr. Parminder Kaur

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Various Frequent Item Set on Data Mining Technique

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Abstract

As with the progression of the IT technological innovation, the quantity of gathered information is also increasing. It has led to lots of information saved in information source, manufacturing facilities and other databases. Thus the Data exploration comes into picture to discover and evaluate the information source to draw out the interesting and previously unidentified styles and rules known as organization concept exploration. This document has focused on regular itemset related based aprioi methods. The overall purpose is to find various restrictions of current methods. The regular itemset exploration has found to be crucial and most expensive step in organization concept exploration. Mining regular styles from extensive information source has appeared as an important problem in information exploration and knowledge finding community. This document ends up with suitable future guidelines to improve the regular item set further.

References

11 Cites in Article

Reference Format

Archana Singh,Jyoti Agarwal (2014). Proposed algorithm for frequent item set generation.
Sallam Fageeri,Rohiza Ahmad,Baharum Baharudin (2014). A semi-apriori algorithm for discovering the frequent itemsets.
Wei Zhang,Hongzhi Liao,Na Zhao (2008). Research on the FP Growth Algorithm about Association Rule Mining.
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Mohamad Basheer,Al-Maqaleh,Saleem Khalid,Shaab (2013). An Efficient Algorithm for Mining Association Rules using Confident Frequent Itemsets.
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Patel Tushar,S Mayur,Ladumor Dhara Unknown Title.
Patel Tushar,S Mayur,Ladumor Dhara,Kapadiya Jahnvi,Prajapati Desai Piyusha,Prajapati Ashish,Reecha (2013). An Analytical Study of Various Frequent Itemset Mining Algorithms.
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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

Manisha Kundal. 2015. \u201cVarious Frequent Item Set on Data Mining Technique\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 15 (GJCST Volume 15 Issue C4): .

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GJCST Volume 15 Issue C4
Pg. 25- 35

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

H.2.8

Submission ReceivedDecember 11, 2014
Peer Review Double Blind
Handling Editor
Accepted January 5, 2015
Published January 15, 2015

Version of record

v1.2

Issue date

June 19, 2015

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Article Matrices

Total Score: 107

Country: India

Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering

Authors: Manisha Kundal, Dr. Parminder Kaur (PhD/Dr. count: 1)

View Count (all-time): 277

Total Views (Real + Logic): 8092

Total Downloads (simulated): 2165

Publish Date: 2015 06, Fri

Monthly Totals (Real + Logic):

Month 1: 63 views
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Total Views: 8092

Total Downloads: 2165

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