Name
Title | ||
|---|---|---|
A parameter-free barebones particle swarm algorithm for unsupervised pattern classification |
Abstract | ||
|---|---|---|
This paper introduces an efficient algorithm for unsupervised clustering that is based on barebones Particle Swarm BB. The proposed algorithm introduces significant enhancement to the Particle Swarm Optimization PSO by eliminating the parameters tuning. The Algorithm aims at finding the centroids of predefined number of clusters where each centroid attracts similar patterns. This research tests and investigates the application of the proposed algorithm to the problem of unsupervised pattern classification by applying the algorithm to segmentations of different images. Experimental results show that the the proposed BB-based algorithm outperforms other state-of-the-art clustering algorithms on all the different levels of comparison. The impact of eliminating the parameters tuning is evident on the performance of the algorithm. In addition, the influence of different values for the swarm size of BB on performance is also illustrated. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.3233/HIS-2012-0152 | Int. J. Hybrid Intell. Syst. |
Keywords | Field | DocType |
parameter-free barebones particle swarm,parameters tuning,different level,proposed bb-based algorithm,barebones particle swarm bb,unsupervised pattern classification,particle swarm optimization pso,state-of-the-art clustering algorithm,different image,efficient algorithm,proposed algorithm,different value | Particle swarm optimization,Canopy clustering algorithm,Cluster (physics),Pattern recognition,Swarm behaviour,Computer science,Multi-swarm optimization,Artificial intelligence,Particle swarm algorithm,Cluster analysis,Centroid,Machine learning | Journal |
Volume | Issue | Citations |
9 | 3 | 1 |
PageRank | References | Authors |
0.36 | 18 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Salah al-Sharhan | 1 | 106 | 13.21 |
| M. G. H. Omran | 2 | 1 | 0.70 |