Discovering Clusters in Spatial Data using Swarm Intelligence

The purpose of this work is to investigate the use of new swarm intelligence based techniques for data mining. According to this approach the data mining task is constructed as a set of biologically inspired agents. Each agent represents a simple task and the success of the method depends on the cooperative work of the agents. In this paper, we present a novel algorithm that uses techniques adapted from models originating from biological collective organisms to discover clusters of arbitrary shape, size and density in spatial data. The algorithm combines a smart exploratory strategy based on the movements of a flock of birds with a shared nearest neighbour clustering algorithm to discover clusters in parallel. In the algorithm, birds are used as agents with a exploring behaviour foraging for clusters. Moreover, this strategy can be used as a data reduction technique to perform efficiently approximate clustering. We have applied this algorithm on synthetic and real world datasets and we have measured, through computer simulation, the impact of the flocking search strategy on performance.