A Survey on Data Mining Algorithm for Market Basket Analysis

Dr. M. Dhanabhakyam

A Survey on Data Mining Algorithm for Market Basket Analysis

Dr. M. Dhanabhakyam * §

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Bharathiar University

A Survey on Data Mining Algorithm for Market Basket Analysis

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Abstract

Abstracts -Association rule mining identifies the remarkable association or relationship between a large set of data items. With huge quantity of data constantly being obtained and stored in databases, several industries are becoming concerned in mining association rules from their databases. For example, the detection of interesting association relationships between large quantities of business transaction data can assist in catalog design, cross-marketing, lossleader analysis, and various business decision making processes. A typical example of association rule mining is market basket analysis. This method examines customer buying patterns by identifying associations among various items that customers place in their shopping baskets. The identification of such associations can assist retailers expand marketing strategies by gaining insight into which items are frequently purchased jointly by customers. It is helpful to examine the customer purchasing behavior and assists in increasing the sales and conserve inventory by focusing on the point of sale transaction data. This work acts as a broad area for the researchers to develop a better data mining algorithm. This paper presents a survey about the existing data mining algorithm for market basket analysis.

References

16 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. M. Dhanabhakyam. . "A Survey on Data Mining Algorithm for Market Basket Analysis". Global Journal of Computer Science and Technology GJCST Volume 11 (GJCST Volume 11 Issue 11).

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GJCST Volume 11 Issue 11

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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

GJCST Classification H.2.8

Submission ReceivedApril 13, 2011
Peer Review Double Blind
Handling Editor
Accepted May 12, 2011
Published May 26, 2011

Version of record

v1.2

Issue date

July 6, 2011

Language

English

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Article Matrices

Total Score: 152

Country: India

Subject: Global Journal of Computer Science and Technology

Authors: Dr. M. Dhanabhakyam,Dr. M. Punithavalli (PhD/Dr. count: 2)

View Count (all-time): 268

Total Views (Real + Logic): 5573

Total Downloads (simulated): 419

Publish Date: 2011 05, Thu

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Total Views: 5.6K

Total Downloads: 419

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