Open AccessA Comparative Analysis of Association Rule Mining Algorithms
Author(s) -
Akash Saxena,
Vikram Rajpoot
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1099/1/012032
Subject(s) - pace , association rule learning , apriori algorithm , computer science , data mining , implementation , a priori and a posteriori , field (mathematics) , algorithm , base (topology) , machine learning , mathematics , mathematical analysis , philosophy , geodesy , epistemology , pure mathematics , programming language , geography
The field of data mining (DM) has grown rapidly in recent years. One of the most important data mining techniques is association rule mining (ARM). It is a strategy used to identify trends in the database that are normal. There has been a lot of work in the area of ARM. The paper provides a short description of the principles and algorithms of interaction, several of the implementations. To several researchers, ARM has long been and still is of concern. Data mining is one of the essential activities. This helps to identify associations between various elements in the database. The goal of this paper is to provide an outline of the fundamental concepts of the ARM methodology and the recent relevant research in this area. The paper further explains the different algorithms, methods, strategies, and benefits of the ARM areas, drawbacks. The paper also provides a minor distinction focused on the results of various algorithms related to association rules mining. The paper provides a short description of the principles and algorithms of interaction, several of the implementations. Algorithms are present and evaluate base parameters such as precision, algorithm pace, and help for data. To solve the question of apriori algorithms AprioriTid and the AprioriHybrid have been suggested. From the contrast, we infer that, since it has decreased overall pace and increased precision, AprioriHybrid is superior to Apriori and AprioriTid. We may infer that the LogElcat algorithm performs more than every other algorithm based on these parameters.