Paper Title
Horizontal Approach to Discover Association Rules

Abstract
This paper introduces an approach to discover association rules by processing records (rows) in a given dataset. We show that while the run time of the classical approach is 〖O(2〗^m) (where m is the number of attributes in the dataset), our approach yields to O(2^(m/2) ) in the worst case. In this paper, we describe the algorithm, provide examples, and discuss the mathematical behaviour of the algorithm. Keywords - Association Rules, Support, Confidence, Itemsets, Performance Analysis, Hypercube Approach