In this paper, we remove the ant-metaphor from ant-based clustering using a randomised partitioning method followed by an agglomerative clustering procedure. While our model only adopts part of the ant-based heuristics, it has produced results that are comparable to the ant-based model. Our approach is based on the fact that one ant can produce the same results as many ants in the models that we have studied, and these models function like stochastic sampling algorithms. In addition, we introduce a schedule to terminate the clustering process before the maximum number of iterations has been reached. We also improve the runtime stability of our model with respect to changes in the structures of the data sets.
['Swee Chuan Tan', 'Kai Ming Ting', 'Shyh Wei Teng']
Reproducing the Results of Ant-based Clustering Without Using Ants