Clustering is a separation of data into collections of parallel objects. Signifying the data by smaller amount of clusters automatically loses certain fine details, but attains simplification. It models data by its clusters. This paper aims to present a brief survey and comparative study on and based clustering theory based on distributed databases in which the goal is to minimize the amount of iterations and cluster sizes is needed to re-optimize the solution when the cluster changes. Number of relative studies namely hybrid, density, Pheromone based ant clustering and cluster analysis. To conclude the discussion, the ant based clustering algorithms are discussed and evaluate the processing time performance on the several distributed datasets. Comparing to these algorithms the efficient Ant based Multiple Pheromone techniques methods outperforms having better performance than other methods

Clustering, partitioning, data mining, Ant clustering, Particle Swarm Optimization

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