The dynamics of collective sorting robot-like ants and ant-like robots
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
Data mining: concepts and techniques
Data mining: concepts and techniques
Ant Colony Optimization
Ant-Based Clustering and Topographic Mapping
Artificial Life
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning and Intelligent Optimization
A novel clustering approach: Artificial Bee Colony (ABC) algorithm
Applied Soft Computing
Population-based artificial immune system clustering algorithm
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
A survey: hybrid evolutionary algorithms for cluster analysis
Artificial Intelligence Review
A particle swarm embedding algorithm for nonlinear dimensionality reduction
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
PREACO: A fast ant colony optimization for codebook generation
Applied Soft Computing
ABK-means: an algorithm for data clustering using ABC and K-means algorithm
International Journal of Computational Science and Engineering
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Data clustering is one of important research topics of data mining. In this paper, we propose a new clustering algorithm based on ant colony optimization, called Ant Colony Optimization for Clustering (ACOC). At the core of the algorithm we use both the accumulated pheromone and the heuristic information, the distances between data objects and cluster centers of ants, to guide artificial ants to group data objects into proper clusters. This allows the algorithm to perform the clustering process more effectively and efficiently. Due to the nature of stochastic and population-based search, the ACOC can overcome the drawbacks of traditional clustering methods that easily converge to local optima. Experimental results show that the ACOC can find relatively good solutions.