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Predicting Missing Items in Shopping Carts using Fast Algorithm

by Srivatsan. M, Sunil Kumar. M, Vijayshankar. V, Leela Rani P
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 21 - Number 5
Year of Publication: 2011
Authors: Srivatsan. M, Sunil Kumar. M, Vijayshankar. V, Leela Rani P
10.5120/2504-3385

Srivatsan. M, Sunil Kumar. M, Vijayshankar. V, Leela Rani P . Predicting Missing Items in Shopping Carts using Fast Algorithm. International Journal of Computer Applications. 21, 5 ( May 2011), 35-41. DOI=10.5120/2504-3385

@article{ 10.5120/2504-3385,
author = { Srivatsan. M, Sunil Kumar. M, Vijayshankar. V, Leela Rani P },
title = { Predicting Missing Items in Shopping Carts using Fast Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { May 2011 },
volume = { 21 },
number = { 5 },
month = { May },
year = { 2011 },
issn = { 0975-8887 },
pages = { 35-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume21/number5/2504-3385/ },
doi = { 10.5120/2504-3385 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:07:45.085719+05:30
%A Srivatsan. M
%A Sunil Kumar. M
%A Vijayshankar. V
%A Leela Rani P
%T Predicting Missing Items in Shopping Carts using Fast Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 21
%N 5
%P 35-41
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Prediction in shopping cart uses partial information about the contents of a shopping cart for the prediction of what else the customer is likely to buy. In order to reduce the rule mining cost, a fast algorithm generating frequent itemsets without generating candidate itemsets is proposed. The algorithm uses Boolean vector with relational AND operation to discover frequent itemsets and generate the association rule. Association rules are used to identify relationships among a set of items in database. Initially Boolean Matrix is generated by transforming the database into Boolean values. The frequent itemsets are generated from the Boolean matrix. Then association rules are to generated from the already generated frequent itemsets. The association rules generated form the basis for prediction. The incoming itemset i.e the content of incoming shopping cart will also be represented by a Boolean vector and AND operation is performed with each transaction vector to generate the association rules. Finally the rules are combined to get the predictions. Dempster’s rule of combination (DRC) is used to combine the evidences. Finally the predicted items are suggested to the user.

References
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  2. M.Anandhavalli, Sandip Jain, Abhirup Chakraborti, Nayanjyoti Roy and M.K.Ghose “Mining Association Rules Using Fast Algorithm”, Advance Computing Conference (IACC), 2010 IEEE 2nd International.
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  8. W. Li, J. Han, and J. Pei, “CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules,” Proc. IEEE Int’l Conf. Data Mining (ICDM ’01), pp. 369-376, Nov./Dec. 2001. M. Kubat, A. Hafez, V.V. Raghavan, J.R. Lekkala, and W.K. Chen, “Itemset Trees for Targeted Association Querying,” IEEE Trans. Knowledge and Data Eng., vol. 15, no. 6, pp. 1522-1534, Nov./Dec.2003.
Index Terms

Computer Science
Information Sciences

Keywords

Association Rule Mining. Boolean Vector Prediction Basic Belief Assignment Demster Shafer Theory of Rule Combination.

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