Title : An evolutionary algorithm based on quantum swarm theory for mining associations in huge datasets
Author : Mohammad Abdul Hafeez, Shaik Abeed Basha, Gudura Raveendrababu, Indlapa Divya
Abstract :
Association rule mining seeks to extract the fundamental cause structure between a set of commonly recurring items or traits in a database. These relationships are represented by rules. Association rule mining provides a strong, non-linear method of associations. The search for link rules is an NP complete problem. Most of the challenges arise when trying to exploit a very large number of database transactions and objects. In this article, we propose a new approach to efficiently deriving the most applicable principles without obligatorily ensuring optimal responses in all circumstances. The innovative derived algorithm is created using the Quantum Swarm Evolutionary technique, which yields better results than genetic algorithms
