Open AccessAn Efficient Method for Mining Distributed Frequent Itemsets: MDFI
Author(s) -
Houda Essalmi et. al.
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i5.1732
Subject(s) - computer science , overhead (engineering) , context (archaeology) , synchronization (alternating current) , data mining , scheme (mathematics) , a priori and a posteriori , computation , apriori algorithm , distributed computing environment , distributed computing , association rule learning , algorithm , computer network , mathematics , paleontology , channel (broadcasting) , mathematical analysis , philosophy , epistemology , biology , operating system
Discovering frequent Itemsets is an interesting problem in the context of parallel and distributed databases. Computation cost and communication/synchronization overhead are important elements in distributed Frequent Itemsets. In this work, we propose an efficient algorithm for mining distributed frequent Itemsets (MDFI) which can significantly reduce the number of candidates Itemsets and communication costs by adopting a Master/Slaves scheme of communication. We present performance comparisons for our algorithm against Apriori and FP-growth algorithms using two datasets with different minimum support.