Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Frequent ITEMSET Mining Map Reduce

Author : Sanchita Sonar 1 Shweta Kawad 2 Karishma Murudkar 3 Prof.Mrs.Sandhya S.Waghere 4

Date of Publication :27th November 2017

Abstract: Data mining deals with the extraction of hidden predictive information from large databases. Main task of data mining is to discover action rules to take profitable decisions from huge database. However, with the exposing recent growth of size of data it is challenging to find out patterns of dataset. We have used a scalable approach for discovering action rules. The main goal of this algorithm is to build a mechanism that enables au-tomatic parallelization and data distribution for parallel mining of frequent itemsets on large clusters. We are proposing an algorithm inspired from FiDoop which run in-house Hadoop cluster. We are distributing dataset into number of blocks as each block allotted to each node for mining purpose. In this algorithm, we have pro- posed two parts, first part finds the frequent itemset in the dataset while the second part optimizes the efficiency of output. We also have optimized the mapper which gives high performance.

Reference :

    1. Yaling Xun, Jifu Zhang, and Xiao Qin, “Fi- Doop: Parallel Mining of Frequent Itemsets Using MapReduce” in IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2016
    2. Sandhya S Waghere, Pothuraju Rajarajeswari, “Parallel Frequent Dataset Mining and Feature Subset Selection for High Dimensional Data on Hadoop using Map-Reduce” in International Journal of Applied Engineering Research ISSN 0973-4562 Volume 12, Number 18 (2017) pp. 7783-7789.
    3. Sandhya S. Waghere, Pothuraju Rajarajeswari, “A Survey on Achieving Best Knowledge from Frequent Item set Mining using Fidoop” in In- ternational Journal of Computer Applications (0975 – 8887) Volume 171 – No. 9, August 2017.
    4. lugendra Dongre, Gend Lal Prajapati, S. V. Tokekar, “The Role of Apriori Algorithm for Finding the Association Rules in Data Mining” in 2014 International Conference on Issues and Challenges in Intelligent Computing Tech-niques (ICICT)
    5. Angelina A. Tzacheva, midhun M.Sunny and Pranava Mummoju , “ MR-Apriori Count Dis-tribution Aigorithm for Parallel Action Rules Discovery” in 2016 IEEE International Confer-ence on Knowledge Engineering and Applica-tions
    6. Sandhya Harikumar, Divya Usha Dilipkumar, “Apriori Algorithm for Association Rule Min-ing in High Dimensional Data” in 2016 IEEE International Conference on Data Science and Engineering (ICDSE)

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