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-neighbor clustering algorithm to discover clusters in parallel. In the algorithm, birds are used as agents with an exploring behavior foraging for clusters. Moreover, this strategy can be used as a data reduction technique to perform approximate clustering efficiently. We have applied this algorithm on synthetic and real world data sets and we have measured, through computer simulation, the impact of the flocking search strategy on performance.
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