Particle Swarm Optimization Based Hierarchical Agglomerative Clustering

Clustering- an important data mining task, which groups the data on the basis of similarities among the data, can be divided into two broad categories, partitional clustering and hierarchal. We combine these two methods and propose a novel clustering algorithm called Hierarchical Particle Swarm Opti...

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Veröffentlicht in:2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Jg. 2; S. 64 - 68
Hauptverfasser: Alam, Shafiq, Dobbie, Gillian, Riddle, Patricia, Naeem, M. Asif
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.08.2010
Schlagworte:
Accuracy
Clustering algorithms
Error analysis
Mathematical models
Merging
Optimization
Particle swarm optimization
Partitioning algorithms
Sensitivity
Vectors
ISBN:9781424484829, 1424484820
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Beschreibung
Zusammenfassung:Clustering- an important data mining task, which groups the data on the basis of similarities among the data, can be divided into two broad categories, partitional clustering and hierarchal. We combine these two methods and propose a novel clustering algorithm called Hierarchical Particle Swarm Optimization (HPSO) data clustering. The proposed algorithm exploits the swarm intelligence of cooperating agents in a decentralized environment. The experimental results were compared with benchmark clustering techniques, which include K-means, PSO clustering, Hierarchical Agglomerative clustering (HAC) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The results are evidence of the effectiveness of Swarm based clustering and the capability to perform clustering in a hierarchical agglomerative manner.
ISBN:9781424484829
1424484820
DOI:10.1109/WI-IAT.2010.75