Parallel Fuzzy Aco Using Classification and Cluster Mining

Abstract

The main purpose of data mining is to extract the required knowledge from data. Data mining is considered as an inter-disciplinary field. Some of the data mining tasks include classification, regression, clustering, and dependence modeling and so on. The first and foremost step is to design the data mining algorithm for which task the algorithm is to be solved. 

The parallel Fuzzy ACO technique is applied to the data mining tasks of both classification and clustering. The optimization algorithm to partition or creating clusters of data is more efficient. Depending on the nature of data when the level of uncertainty composition is higher in a given data set, parallel fuzzy ACO for classification and clustering gets complicated.

The proposed Parallel Fuzzy ACO search procedure is used in the structure of an evolutionary fuzzy system. The capability of the resulted system is investigated according to the classification problem.The clustering approach established in our work Parallel fuzzy ACO for clustering provides a framework for optimization.