Journal of Advances in Mathematics and Computer Science, ISSN: 2456-9968, ISSN: 2231-0851 (Past),Vol.: 27, Issue.: 1
Improved FTWeightedHashT Apriori Algorithm for Big Data using Hadoop-MapReduce Model
Sarem M. Ammar1* and Fadl M. Ba-Alwi2 1Departement of IT, Yemen Academic for Graduate Studies, Yemen. 2Faculty of Computer & IT, Sana’a University, Yemen.
Sarem M. Ammar1* and Fadl M. Ba-Alwi2
1Departement of IT, Yemen Academic for Graduate Studies, Yemen.
2Faculty of Computer & IT, Sana’a University, Yemen.
(1) Francisco Welington de Sousa Lima, Professor, Dietrich Stauffer Laboratory for Computational Physics, Departamento de Física, Universidade Federal do Piaui, Teresina, Brazil.
(2) Dariusz Jacek Jakóbczak, Assistant Professor, Chair of Computer Science and Management in this department, Technical University of Koszalin, Poland.
(1) Carson Leung, University of Manitoba, Canada.
(2) Sudhakar Singh, Institute of Science, Banaras Hindu University, India.
(3) Ishwar Kalbandi, JSPM'S Jayawantrao Sawant College of Engineering, India.
(4) Enrique Lazcorreta Puigmartí, Institute Center of Operations Research, Miguel Hernández University of Elche, Spain.
Complete Peer review History: http://www.sciencedomain.org/review-history/24053
The most significant problem of data mining is the frequent itemset mining on big datasets. The best-known basic algorithm for frequent mining itemset is Apriori. Due to the drawbacks of Apriori algorithm, many improvements have been done to make Apriori better, efficient and faster. We have reviewed over 100 papers related to this work that include enhancements be done to improve Apriori algorithm. Weighted based Apriori and Hash Tree based Apriori are the most significant improvements. One of the recent papers integrated the weight concept of weighted Apriori and Hash tree construction concept of Hash Tree Apriori to produce a hybrid Apriori algorithm named WeightedHashT. In this paper, we aim to propose a new approach to improve WeightedHashT Apriori algorithm on big data using Hadoop-MapReduce model by employing the transaction filtering technique. The experiment of this work using different datasets manifests that the proposed algorithm is efficient and effective regarding execution time.
Big data; hadoop; mapreduce; apriori; frequent itemset mining.
DOI : 10.9734/JAMCS/2018/39635Review History Comments