An Adaptive Flocking Algorithm for Spatial Clustering

This paper presents a parallel spatial clustering algorithm based on the use of new Swarm Intelligence (SI) techniques. SI is an emerging new area of research into Artificial Life, where a problem can be solved using a set of biologically inspired (unintelligent) agents exhibiting a collective intelligent behaviour. The algorithm, called SPARROW, combines a smart exploratory strategy based on a flock of birds with a density-based cluster algorithm to discover clusters of arbitrary shape and size in spatial data. Agents use modified rules of the standard flock algorithm to transform an agent (boid) into a hunter foraging for clusters in spatial data. We have applied this algorithm to two synthetic data sets and we have measured, through computer simulation, the impact of the flocking search strategy on performance. Moreover, we have evaluated the accuracy of SPARROW compared to the DBSCAN algorithm.