Parallel processing of frequent itemset based on MapReduce programming model

Document Type

Conference Proceeding

Publication Title

2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA)

Publisher

IEEE

School

School of Science

Comments

Deshmukh, R. A., Bharathi, H. N., & Tripathy, A. K. (2019, September). Parallel processing of frequent itemset based on MapReduce programming model [Paper presentation]. 2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA), Pune, India. https://doi.org/10.1109/ICCUBEA47591.2019.9128369

Abstract

Searching frequent itemset in large size diverse database is one of the most important data mining problem and as existing algorithms are insufficient in mechanism that enables automatic parallelization, fault tolerance and data distribution. Solution to this issue we design algorithm using MapReduce programming model. The overarching aim is to enhance the performance of parallel frequent itemset mining on Hadoop. Incorporating ultra-metric tress to improve more efficiency of mining frequent itemset and comparing Apriori algorithm and FP-Growth algorithm based on some parameters. We implement the algorithm with dataset of Market Basket Analytics.

DOI

10.1109/ICCUBEA47591.2019.9128369

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