Abstract—These instructions Frequent itemsets mining is one of the most important and crucial part in today’s world for every transactional database. Many researchers have introduced many algorithms for mining frequent itemsets over the last few decades. Firstly the most known and powerful horizontal database layout based algorithm introduced is Apriori algorithm then various improvements also been introduced on the basis of this approach. Then the tree projection based algorithms are introduced for the efficient storage and retrieval of the datasets. Tree projection based algorithms include FP-Growth, H-Mine and many more. Various hybrid algorithms also been introduced for taking the advantage of vertical as well as tree projection algorithms. This classification aims to enhance the understanding of various present techniques and direction of research in this area. This article attempts to provide theoretical aspects of the key techniques.

Index Terms—Data mining, Frequent itemsets, Apriori algorithms, FP-Tree algorithms.

The authors are with Computer Science Department, Thapar University, Punjab, India. (e-mail:bharatgupta35@gmail.com)

Cite: Bharat Gupta and Deepak Garg, "A Taxonomy of Classical Frequent Item set Mining Algorithms," International Journal of Computer and Electrical Engineering vol. 3, no. 5, pp. 695-699, 2011.