Different Approaches for Frequent Itemset Mining

P.V. Nikam¹ , D.S. Deshpande²

1. Department of CSE, Jawaharlal Nehru Engineering College, Aurangabad, India.
2. Department of CSE, Jawaharlal Nehru Engineering College, Aurangabad, India.

Correspondence should be addressed to: pallavinikam19@gmail.com.

Section:Research Paper, Product Type: Isroset-Journal
Vol.6 , Issue.2 , pp.10-14, Apr-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i2.1014

Online published on Apr 30, 2018

Copyright © P.V. Nikam, D.S. Deshpande . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Data mining is the retrieval of hidden analytical information from huge databases, is a controlling new technology with great possible to help organizations as well as research focus on the mainly essential information in their data warehouses. Data mining tools forecast future development and performances, allowing businesses to create proactive, idea for decision making systems. Frequent Itemset Mining (FIM) is one of the traditional data mining problems in mainly of the data mining approaches. It requires very huge computations and input and output traffic capacity. Also resources like single processor’s memory and CPU are very limited, which degrades the presentation of algorithm. In this research work system proposed one such distributed approach which will run on Hadoop cluster – one of the recent most popular distributed frameworks which basically focus on parallel processing. The proposed framework takes into account extends characteristics of the Apriori algorithm related to the frequent itemset invention and throughout a block-based partitioning uses a dynamic workload management. The algorithm greatly improves the performance and gets high scalability compared to the existing approaches. Proposed algorithm is implemented and tested on large scale datasets distributed system on heterogeneous cluster.

Key-Words / Index Term :
Frequent Itemset, Apriori, FP Growth, Modified Apriori

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