An Adaptive Flocking Algorithm for Spatial Clustering

  • Gianluigi Folino
  • Giandomenico Spezzano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2439)


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 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.


Spatial Data Spatial Cluster Swarm Intelligence Artificial Life Core Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Gianluigi Folino
    • 1
  • Giandomenico Spezzano
    • 1
  1. 1.ICAR-CNRUniversitá della CalabriaRendeItaly

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