Prioritizing Effectiveness of Algorithms of Association Rule Mining
Keywords:
data mining, big data, association rule mining, ARM algorithms, systematic literature review.Abstract
From the last decade, clouds have become the most popular platform for data storage. In the current age, people and machines are engaged in transferring data on clouds. The devices like cameras, computers, mobile, and CCTV are being used to transmit data collectively on the clouds in each second. Such a huge collection of data on clouds is known as Big data. Data mining is a process of extracting useful information from a set of huge data. The different techniques like Association Rule Mining, Classification, and Clustering are some of the well-known techniques, which can be used for data mining purposes. Association Rule Mining is a process of mining associations and correlations among the items in a large data set. Some traditional algorithms like Apriori, FP-growth, and Eclat are being used for association rule mining, but no one provides an optimal solution. In our study, we ascertained the working algorithms, evaluated their performance, and finally ranked them based on their efficiency. We adopted a quantitative approach in our research. We framed queries, pinpointed pertinent work, gauged quality, summarized the evidence, and finally interpreted our findings.
Downloads
Published
Issue
Section
License
Copyright (c) 2021 Journal of Computational Learning Strategies & Practices
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The journal is open access. Reading, downloading, copying, distributing and use of any material for academic and research purposes is free. The copyright in the Journal is owned by the CLSP. Unauthorized copying or redistribution for any financial or earning purpose will be violation of copyright laws. Moreover, managing editor is not responsible for originality of the articles accepted for the journal. However, the authors would be accountable if the ideas and the materials are found plagiarized. The journal is in the process of licensing under a Creative Commons Attribution- Non Commercial 4.0 International License. All articles published by JCLSP will be licensed under the Creative Commons Attribution 4.0 International License. This permits anyone to copy, redistribute, transmit and adapt the work provided the original work and source is appropriately cited as specified by the Creative Commons Attribution License.