Discovering Clusters in Spatial Data using Swarm Intelligence (Contributo in atti di convegno)

Type

• Contributo in atti di convegno (Classe)
• Prodotto della ricerca (Classe)

Label

• Discovering Clusters in Spatial Data using Swarm Intelligence (Contributo in atti di convegno) (literal)

Anno

• 2003-01-01T00:00:00+01:00 (literal)

Alternative label

• Folino Gianluigi, Forestiero Agostino, Spezzano Giandomenico (2003)
  Discovering Clusters in Spatial Data using Swarm Intelligence
  in Advances in Artificial Life, 7th European Conference, ECAL 2003
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Folino Gianluigi, Forestiero Agostino, Spezzano Giandomenico (literal)

Pagina inizio

• 0 (literal)

Pagina fine

• 0 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 2801- (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note

• Dortmund, Germania (literal)

Note

• ISI Web of Science (WOS) (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• ICAR-CNR (literal)

Titolo

• Discovering Clusters in Spatial Data using Swarm Intelligence (literal)

Abstract

• 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. (literal)

Prodotto di

• Institute for high performance computing and networking (ICAR) (Istituto)

Autore CNR

• GIANLUIGI FOLINO (Persona)
• GIANDOMENICO SPEZZANO (Unità di personale interno)
• AGOSTINO FORESTIERO (Unità di personale esterno)

Incoming links:

Prodotto

• Institute for high performance computing and networking (ICAR) (Istituto)

Autore CNR di

• GIANDOMENICO SPEZZANO (Unità di personale interno)
• GIANLUIGI FOLINO (Persona)
• AGOSTINO FORESTIERO (Unità di personale esterno)