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Data clustering plays an important role in many disciplines, including data mining, machine learning, bioinformatics, pattern recognition, and other fields. When there is a need to learn the inherent grouping structure of data in an unsupervised manner, ant-based clustering stand out as the most widely used group of swarm-based clustering algorithms. Under this perspective, this paper presents a new Adaptive Ant-based Clustering Algorithm (AACA) for clustering data sets. The algorithm takes into account the properties of aggregation pheromone and perception of the environment together with other modifications to the standard parameters that improves its convergence. The performance of AACA is studied and compared to other methods using various patterns and data sets. It is also compared to standard clustering using a set of analytical evaluation functions and a range of synthetic and real data collection. Experimental results have shown that the proposed modifications improve the performance of ant-colony clustering algorithm in term of quality and run time.