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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Vydáno v:2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Ročník 2; s. 64 - 68
Hlavní autoři: Alam, Shafiq, Dobbie, Gillian, Riddle, Patricia, Naeem, M. Asif
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.08.2010
Témata:
Accuracy
Clustering algorithms
Error analysis
Mathematical models
Merging
Optimization
Particle swarm optimization
Partitioning algorithms
Sensitivity
Vectors
ISBN:9781424484829, 1424484820
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Shrnutí: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