A Comprehensive Study on Enhanced Clustering Technique of Association Rules over Transactional Datasets

Abstract

The most well-recognized fields in data mining is association rule mining. It’s been used within various applications including industry baskets, computer networks, recommendation systems and healthcare. Exploratory data analysis and data mining (DM) applications rely heavily on clustering. Cluster analysis seeks to categorize a group of patterns into groups based on their similarity. This paper aims to enhance the clustering technique of association rules over transactional datasets. At the outset the concepts behind association rules are explained followed by an overview of some of the recent research in this field. The benefits and drawbacks are addressed and a conclusion is drawn.