AMRIT PAL
Articles written in Sadhana
Volume 45 All articles Published: 2 July 2020 Article ID 0169
Distributed synthesized association mining for big transactional data
Data is increasing rapidly day by day along with the transactional database. Dividing this data and storing it in a distributed manner is an effective way for storage and retrieval. Mining such distributed data with minimum dependence between sub-problems is a crucial task. Finding frequent itemsets and corresponding association rules is a big challenge while considering the aggregation in a distributed environment. To overcome these challenges, we propose a distributed frequent itemset generation and association rule mining algorithm using MapReduce programming model. The proposed scheme generates frequent itemset and mine association rules using a synthesized distributed technique. The rules are mined in a distributed manner, and then weights are assigned to subsets of data and association rules. A proper mixture of association rules that are generated in distributed manner is done using a weighted approach. This paper presents a novel MapReduce-based synthesisapproach, which can work well over a distributed storage of large amount of data.
Volume 45, 2020
All articles
Continuous Article Publishing mode
Click here for Editorial Note on CAP Mode
© 2021-2022 Indian Academy of Sciences, Bengaluru.