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2017•International Journal of Advanced Research in Computer ScienceOpen access

ANALYSIS OF SUPER MARKET USING ASSOCIATION RULE MINING

Daljeet Kaur

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Abstract

Supermarket analysis is a process to analyze the buyer’s habits to discover the correlations between the different items in their shopping cart. The findings of these correlations can help the retailers to establish a profitable sales strategy by considering frequently purchased items together by customers. Association rule mining is one of the famous data mining techniques used to discover the correlations between one item to another. Association rule mining technique has number of algorithms, but this research focuses on the effectiveness of the combination of the two association rule mining algorithms that are apriori algorithm and eclat algorithm for supermarket analysis. The collaboration of both the algorithms revealed that both methods use the same concept with different criteria of processing the association rules, but the rules itself remains the same.

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What this paper is about

Supermarket analysis is a process to analyze the buyer’s habits to discover the correlations between the different items in their shopping cart. The findings of these correlations can help the retailers to establish a profitable sales strategy by considering frequently purchased items together by customers. Association rule mining is one of the famous data mining techniques used to discover the correlations between one item to another. Association rule mining technique has number of algorithms, but this research focuses on the effectiveness of the combination of the two association rule mining algorithms that are apriori algorithm and eclat algorithm for supermarket analysis. The collaboration of both the algorithms revealed that both methods use the same concept with different criteria of processing the association rules, but the rules itself remains the same.

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OpenAlex reports 2 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Supermarket analysis is a process to analyze the buyer’s habits to discover the correlations between the different items in their shopping cart. The findings of these correlations can help the retailers to establish a profitable sales strategy by considering frequently purchased items together by customers. Association rule mining is one of the famous data mining techniques used to discover the correlations between one item to another. Association rule mining technique has number of algorithms, but this research focuses on the effectiveness of the combination of the two association rule mining algorithms that are apriori algorithm and eclat algorithm for supermarket analysis. The collaboration of both the algorithms revealed that both methods use the same concept with different criteria of processing the association rules, but the rules itself remains the same.

Key concepts: Association rule learning, Affinity analysis, Apriori algorithm, Computer science, Data mining, Cart, Process (computing), Association (psychology)

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