This paper presents a parallel spatial clustering algorithm based on the use of new Swarm Intelligence (SI) techniques. 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.
An Adaptive Flocking Algorithm for Spatial Clustering
Folino G;Spezzano G
2002
Abstract
This paper presents a parallel spatial clustering algorithm based on the use of new Swarm Intelligence (SI) techniques. 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
