
Frequent Itemset Mining Based on Development of FP-growth Algorithm and Use MapReduce Technique
Author(s) -
Zakria Mahrousa,
Dima Mufti Alchawafa,
Hasan Kazzaz
Publication year - 2021
Publication title -
mağallaẗ ittiḥād al-ğāmiʿāt al-ʿarabiyyaẗ li-l-dirāsāt wa-al-buḥūṯ al-handasiyyaẗ
Language(s) - English
Resource type - Journals
eISSN - 2616-9401
pISSN - 1726-4081
DOI - 10.33261/jaaru.2021.28.1.008
Subject(s) - computer science , data mining , big data , task (project management) , graph , process (computing) , execution time , database , algorithm , theoretical computer science , parallel computing , management , economics , operating system
The Finding of frequent itemset in big data is an important task in data mining and knowledgediscovery. The exponential daily growth of data, called “Big Data”, mining frequent patterns from the hugevolumes of data has many challenges due to memory requirement, multiple data dimensions, heterogeneityof data and so on. The complexities related to mining frequent item-sets from a Big Data can be minimizedby using Modified FP-growth algorithm and parallelizing the mining task with Map Reduce framework inHadoop. In this paper, a modified FP-growth based on directed graph with Hadoop framework will reducethe execution time for the massive database and works efficiently on number of nodes (computers). Thealgorithm was tested, our experimental results demonstrated that the proposed algorithm could scale welland efficiently process large datasets. In addition, it achieves improvement in memory consumption to storefrequent patterns and time complexity.