International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 8, No. 6, 8 Nov. 2018
Cover page and Table of Contents: PDF (size: 365KB)
Apriori Algorithm using Hashing for Frequent Itemsets Mining
Full Text (PDF, 365KB), PP.46-58
Views: 0 Downloads: 0
Author(s)
Debabrata Datta
1,*
Atindriya De
2
Deborupa Roy
3
Soumodeep Dutta
4
Index Terms
Apriori algorithm, hashing, frequent itemsets, association rule, support count
Abstract
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
Reference
[1]Sudhir Tirumalasetty, Aruna Jadda, Sreenivasa and Reddy Edara, “An enhanced Apriori Algorithm for Discovering Frequent Patterns with Optimal Number of Scans”, International Journal of Computer Science, 2015.
[2]Ashma Chawla and Kanwalvir Singh Dhindsa, “Implementation of Association Rule Mining using Reverse Apriori Algorithmic Approach”, International Journal of Computer Applications, Vol. 93, No.8, 2014, pp. 24 – 28.
[3]Yanbin Ye and Chia-Chu Chiang, “A parallel Apriori Algorithm for Frequent Itemsets Mining”, In proceedings of the Fourth International Conference on Software Engineering Research, Management and Applications, 2006, pp. 87 – 94.
[4]Raffaele Perego, Salvatore Orlando and P.Palmerini, “Enhancing the Apriori Algorithm for Frequent Set Counting”, In proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery, 2001, pp. 71 – 82.
[5]K.Vanitha and R.Santhi, “Using Hash Based Apriori Algorithm To Reduce The Candidate 2- Itemsets For Mining Association Rule”, Journal of Global Research in Computer Science ,Vol. 2, No. 5, 2011, pp. 78 – 80.
[6]Nick Roussopoulos, “Materialized Views and Data Warehouses”, ACM SIGMOD Newsletter, Vol. 27, Issue 1, 1998, pp. 21 – 26.
[7]Shikha Bhardwaj, Preeti Chhikara, Satender Vinayak, Nishant Pai, Kuldeep Meena, “Improved Apriori Algorithm for Association Rules”, International Journal of Technical Research and Applications, Vol. 3, Issue 3, 2015, pp. 238 – 240.
[8]Surajt Chaudhuri, Umeshwar Dayal, “An overview of Data Warehousing and OLAP technology”, ACM Sigmond Record, Vol. 26, Issue 1, 1997, pp. 65-74.
[9]Jugendra Dongre, Gend Lai Prajapati, S.V. Tokekar “The role of Apriori Algorithm for finding the association rules in Data Mining”, In proceedings of International Conference on Issues and Challenges in Intelligent Computing Techniques, 2014.
[10]Sheila A. Abaya “Association rule mining based on Apriori algorithm in minimizing candidate generation”, In proceedings of International Journal of Scientific and Engineering Research, Vol. 3, Issue 7, pp. 171-174, 2012.
[11]S.Appavu, Aravind, Athiappan, Bharathiraja, Muthu Pandian and Dr.R.Rajaram, “Association rule mining for suspicious Email detection:A data mining approach”, IEEE Intelligence and Security Informatics, 2007.