Various Frequent Item Set on Data Mining Technique

Manisha Kundal

Various Frequent Item Set on Data Mining Technique

Manisha Kundal *

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parminderkaur

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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.
D Goswami,Chaturvedi Anshu,C Raghuvanshi (2010). An Algorithm for Frequent Pattern Mining Based On Apriori.
Mohamad Basheer,Al-Maqaleh,Saleem Khalid,Shaab (2013). An Efficient Algorithm for Mining Association Rules using Confident Frequent Itemsets.
Sakshi Saurabh Malgaonkar,Tejas Surve,Hirave (2013). Use of Mining Techniques To Improve The Effectiveness of Marketing and Sales.
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.
Komate Amphawan (2013). Mining top-k regular-frequent itemsets from transactional database.
R Mihir,Dipti Patel,Rupa Rana,Mehta (2013). FApriori: A Modified Apriori Algorithm Based on Checkpoint.
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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, parminderkaur. 2015. "Various Frequent Item Set on Data Mining Technique". 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

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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

Language

English

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Article Matrices

Total Score: 147

Country: India

Subject: Global Journal of Computer Science and Technology

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

View Count (all-time): 406

Total Views (Real + Logic): 3214

Total Downloads (simulated): 197

Publish Date: 2015 01, Thu

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