Apriori Algorithm using Hashing for Frequent Itemsets Mining

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Debabrata Datta 1,* Atindriya De 2 Deborupa Roy 3 Soumodeep Dutta 4

1. St. Xavier’s College (Autonomous), Kolkata, India

2. Jadavpur University, Kolkata, India

3. Calcutta University, Kolkata, India

4. Banaras Hindu University, Varanasi, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijeme.2018.06.05

Received: 31 Mar. 2018 / Revised: 23 May 2018 / Accepted: 6 Aug. 2018 / Published: 8 Nov. 2018

Index Terms

Apriori algorithm, hashing, frequent itemsets, association rule, support count


Data Warehousing, data mining and analysis plays a very important role in decision support. Various commercial organisations are using tools based on these techniques to be used for decision support system. Apriori algorithm is a classic algorithm which works on a set of data in the database and provides us with the set of most frequent itemsets. It is used to find the association rules and mines the most frequent itemsets in a set of transactions. Here the frequent subsets are extended one item at a time. In this paper a hash-based technique with Apriori algorithm has been designed to work on data analysis. Hashing helps in improving the spatial requirements as well as makes the process faster. The main purpose behind the work is to help in decision making. The user will select an item which he/she wishes to purchase, and his/her item selection is analysed to give him/her an option of two and three item sets. He/she can consider choosing a combination of two item sets or three item sets, or he/she can choose to go with his/her own purchase. Either ways, the algorithm helps him in making a decision.

Cite This Paper

Debabrata Datta, Atindriya De, Deborupa Roy, Soumodeep Dutta,"Apriori Algorithm using Hashing for Frequent Itemsets Mining", International Journal of Education and Management Engineering(IJEME), Vol.8, No.6, pp.46-58, 2018. DOI: 10.5815/ijeme.2018.06.05


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