Applying Data Clustering Feature to Speed Up Ant Colony Optimization

Constrained Ant Colony Optimization for Data Clustering

Processes that simulate natural phenomena have successfully been applied to a number of problems for which no simple mathematical solution is known or is practicable. Such meta-heuristic algorithms include genetic algorithms, particle swarm optimization and ant colony systems and have received increasing attention in recent years. This paper extends ant colony systems and discusses a novel data...

Simultaneous feature selection and ant colony clustering

Clustering is a widely studied problem in data mining. Ai techniques, evolutionary techniques and optimization techniques are applied to this field. In this study, a novel hybrid modeling approach proposed for clustering and feature selection. Ant colony clustering technique is used to segment breast cancer data set. To remove irrelevant or redundant features from data set for clustering Sequen...

Ant Colony Optimization Toward Feature Selection

Ant Colony Optimization for Feature Subset Selection

The Ant Colony Optimization (ACO) is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It has recently attracted a lot of attention and has been successfully applied to a number of different optimization problems. Due to the importance of the feature selection problem and the potential of ACO, this paper presents a novel method that ut...

Ant Colony Optimization based clustering methodology

In this work we consider spatial clustering problem with no a priori information. The number of clusters is unknown, and clusters may have arbitrary shapes and density differences. The proposed clustering methodology addresses several challenges of the clustering problem including solution evaluation, neighborhood construction, and data set reduction. In this context, we first introduce two obj...