Comparative Analysis of Various Algorithms in ARM

Abstract

Data mining is a process in which useful information is discovered from large volumes of data using various tasks such as classification, clustering, association rules. Frequent items are the sets of items or structures which occur in a transaction. It gives the information about how frequently the specific item appears in a transaction. Though there are many mining tasks, one of the finest methods is association rule mining which finds the correlation, frequent patterns and rules from a various large amount of dataset. Association rule mining uses various scalable and efficient algorithms which predicts the rules to find the occurrence of an element in the dataset. This paper compares various association rule mining algorithms based on the data support and speed.