On Improving Clustering in Numerical Databases with Artificial Ants

We present in this paper a new hybrid algorithm for data clustering. This algorithm discovers automatically clusters in numerical data without prior knowledge of a possible number of cleisses, without any initial partition, and without complex parameter settings. It uses the stochastic eind exploratory principles of an ant colony with the deterministic and heuristic principles of the K-means cJgorithm. Ants move on a 2D bosird and may load or drop objects. Dropping aa object on an existing heap of objects depends on the similarity between this object and the heap. The K-means algorithm improves the convergence of the ant colony clustering. We repeat two stochastic/deterministic steps and introduce hierarchical clustering on heaps of objects and not just objects. We also use other refinements such as aji heterogeneous population of ants to avoid complex parameters settings, and a local memory in each ant. We have applied this algorithm on standard databases cind we get very good results compared to the K-means and ISODATA algorithms.