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2014•Unpublished venueRequires access

An efficient filtration approach for mining association rules

Lalit Mohan Goyal, M. M. Sufyan Beg

Open publisher page 3 citations

Abstract

Association rule mining (ARM) is a well-researched domain in the field of data mining. It is seen as a problem of predicting customers purchasing behavior, popularly known as “Market Basket Analysis”. This problem can be solved by using Apriori algorithm which is majorly 3-steps (Joining, Pruning and Verification) process. In this paper, an alternate to Apriori algorithm's pruning step is proposed. This alternative is depicted as a filtration step.

About this research paper

What this paper is about

Association rule mining (ARM) is a well-researched domain in the field of data mining. It is seen as a problem of predicting customers purchasing behavior, popularly known as “Market Basket Analysis”. This problem can be solved by using Apriori algorithm which is majorly 3-steps (Joining, Pruning and Verification) process. In this paper, an alternate to Apriori algorithm's pruning step is proposed. This alternative is depicted as a filtration step.

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OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Association rule mining (ARM) is a well-researched domain in the field of data mining. It is seen as a problem of predicting customers purchasing behavior, popularly known as “Market Basket Analysis”. This problem can be solved by using Apriori algorithm which is majorly 3-steps (Joining, Pruning and Verification) process. In this paper, an alternate to Apriori algorithm's pruning step is proposed. This alternative is depicted as a filtration step.

Key concepts: Association rule learning, Apriori algorithm, Affinity analysis, Pruning, Data mining, Computer science, Field (mathematics), A priori and a posteriori

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