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In this paper, we propose a new method for constructing decision trees based on Ant Colony Optimization (ACO). The ACO is a metaheuristic inspired by the behavior of real ants, where they search for optimal solutions by considering both local heuristic and previous knowledge, observed by pheromone changes. Good results of the ant colony algorithms for solving combinatorial optimization problems suggest...
We present in this paper a modification of Lumer and Faieta’s algorithm for data clustering. This approach mimics the clustering behavior observed in real ant colonies. This algorithm discovers automatically clusters in numerical data without prior knowledge of possible number of clusters. In this paper we focus on ant-based clustering algorithms, a particular kind of a swarm intelligent system, and...
We present in this paper a modification of Lumer and Faieta's algorithm for data clustering. This algorithm discovers automatically clusters in numerical data without prior knowledge of possible number of clusters. We have applied this algorithm on standard databases and we get very good results compared to the AntClass, k-means and ISODATA algorithms for IRIS dataset
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