Mining Association Rules in Various Computing Environments: A Survey

TL;DR

This survey reviews the evolution of association rule mining algorithms across various computing environments, highlighting advancements from sequential to cloud computing to improve efficiency.

Contribution

It provides a comprehensive overview of ARM algorithms in different computing paradigms, emphasizing their development and adaptation to new technologies.

Findings

ARM algorithms have evolved significantly across computing environments.

Parallel and distributed algorithms improve efficiency over sequential methods.

The survey identifies trends and future directions in ARM research.

Abstract

Association Rule Mining (ARM) is one of the well know and most researched technique of data mining. There are so many ARM algorithms have been designed that their counting is a large number. In this paper we have surveyed the various ARM algorithms in four computing environments. The considered computing environments are sequential computing, parallel and distributed computing, grid computing and cloud computing. With the emergence of new computing paradigm, ARM algorithms have been designed by many researchers to improve the efficiency by utilizing the new paradigm. This paper represents the journey of ARM algorithms started from sequential algorithms, and through parallel and distributed, and grid based algorithms to the current state-of-the-art, along with the motives for adopting new machinery.