DOI:10.20894/IJDMTA.
Periodicity: Bi Annual.
Impact Factor:
SJIF:4.893 & GIF:0.787
Submission:Any Time
Publisher: IIR Groups
Language: English
Review Process:
Double Blinded

News and Updates

Author can submit their paper through online submission. Click here

Paper Submission -> Blind Peer Review Process -> Acceptance -> Publication.

On an average time is 3 to 5 days from submission to first decision of manuscripts.

Double blind review and Plagiarism report ensure the originality

IJDMTA provides online manuscript tracking system.

Every issue of Journal of IJDMTA is available online from volume 1 issue 1 to the latest published issue with month and year.

Paper Submission:
Any Time
Review process:
One to Two week
Journal Publication:
June / December

IJDMTA special issue invites the papers from the NATIONAL CONFERENCE, INTERNATIONAL CONFERENCE, SEMINAR conducted by colleges, university, etc. The Group of paper will accept with some concession and will publish in IJDMTA website. For complete procedure, contact us at admin@iirgroups.org

Paper Template
Copyright Form
Subscription Form
web counter
web counter
Published in:   Vol. 6 Issue 2 Date of Publication:   December 2017

Market Basket Analysis using Improved FP-tree

Abhishek Priyadarshi,Chirag Gupta, G Poornalatha

Page(s):   37-40 ISSN:   2278-2397
DOI:   10.20894/IJDMTA.102.006.002.001 Publisher:   Integrated Intelligent Research (IIR)

The Market Basket Analysis helps in identifying the purchasing patterns of customers such as, which products are purchased more and which products are purchased together. This helps in decision making process. For example, if two or more products are frequently purchased together then they can be kept at the same place so as to facilitate the customer, to further increase their sale. The price of products that are not frequently purchased can be reduced in order to enhance their purchase. Additionally the promotion of one product will also increase the sales of other products which are purchased together with the product being promoted. The traditional Apriori algorithm based on candidate generation cannot be used in Market Basket Analysis because it generates candidate sets and scans database regularly for the generation of frequent itemsets. The FP-growth algorithm cannot be used despite of the fact that it does not generate candidate sets and scans the database only twice because, it generates a lot of conditional trees recursively. Therefore, an efficient algorithm needs to be used. In this paper an efficient algorithm is used for development of market basket analysis application. This efficient algorithm neither generates candidate sets nor conditional FP- tree; like FP-growth scans the database twice.