Improving Noise Clustering Algorithm Using Ant Colony Optimization

Noise clustering, as a robust clustering method, performs partitioning of data sets reducing errors caused by outliers. In many applications outliers contain important information and their correct identification are crucial. The original ant system algorithm is simplified leading to a generalized ant colony optimization algorithm that can be used to solve a wide variety of discrete optimization problems. It is shown how objective function based clustering models such as noise clustering can be optimized using particular extensions of this simplified ant optimization algorithm. Experiments with artificial dataset show that ant clustering (NC-ACO) produces better results.