A new move towards updating pheromone trail in order to gain increased predictive accuracy in classification rule mining by implementing ACO algorithm

Ant miner algorithm is used to find the classification rule which helps to do classification of the data. Ant miner uses the Ant Colony Optimization (ACO) which deals with artificial systems that is inspired from the foraging behavior of real ants. Here the task is to improve the pheromone update method in the current system. Pheromone updating is dependent mainly on the initial pheromone of the term, and term Q (quality of term) which is added to current accumulated pheromone. In this methods a try is made to lay pheromone on trail such that selection of terms is not biased and unified behavior for the system as a whole, produce a robust system capable of finding high-quality solutions for problems. Here in this approach amount of pheromone added with the used term is not directly dependent on accumulated pheromone but also on the Q (quality of rule). So here in Q is modified and multiplied in ways that help to get better solution. For this use of rule length is done and manipulated in ways that support approach to achieve goal. Thus, aim is to improve the accuracy and sustaining rule list simplicity using Ant Colony Optimization in data mining.