An Approach to Spatial Data Mining Inspired by ... - Semantic Scholar

An Approach to Spatial Data Mining Inspired by Agent Swarming Gianluigi Folino, Agostino Forestiero, Giandomenico Spezzano Institute for High Performance Computing and Networking (ICAR) – CNR Via P. Bucci 41C I-87030 – Rende (CS), Italy {folino, forestiero, spezzano}@icar.cnr.it

1. Introduction The use of new Swarm Intelligence (SI) based techniques [1 ] for data mining task is an emerging new area of research that has attracted many investigators in the last years. 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. An agent represents an entity capable of sensing its environment and undertaking simple processing of environmental observations in order to perform an action chosen from those available to it. These actions may include modification of the environment in which the agent operates. Intelligent behaviour frequently arises through indirect communication between the agents using the principle of stigmergy [2]. Two forms of stigmergy have been observed: sematectonic and sign-based. According to sematectonic stigmergy the only relevant interactions between individuals occur through modifications of the environment. Individual behaviour modifies the environment, which in turn modifies the behaviour of other individuals. The second form of stigmergy is sign-based. Here something is deposited in the environment that makes no direct contribution to the task being undertaken but is used to influence the subsequent behavior that is task related. Sign-based stigmergy is for example used in ants colonies, termites, swarms of bees etc . Clustering spatial data is the process of grouping similar objects according to their distance, connectivity, or their relative density in space [3]. Spatial clustering has been an active area of research into data mining, with many effective and scalable clustering methods developed. These methods can be classified into partitioning methods [4], hierarchical methods [5], density-based methods [6], and grid-based methods [7]. Han, Kamber, and Tung’s paper [8] is a good introduction to this subject. Recently, other algorithms based on biological models [9-13] have been proposed to solve the clustering problem. These algorithms are characterized by the interaction of a large number of simple agents sensing and changing their environment locally. They exhibit complex, emergent behaviour that is robust compared to the failure of individual agents. This paper presents a parallel spatial clustering algorithm based on the use of new Swarm Intelligence techniques. The algorithm, called SPARROW, combines a smart exploratory strategy based on a flock of birds [14] 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 [15].

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