Mahamed G.H. Omran and Andries P Engelbrecht and Ayed Salman
Dynamic Clustering using Particle Swarm Optimization with Application in Unsupervised Image Classification
2898 - 2903
2007
1
9
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/11937
https://publications.waset.org/vol/9
World Academy of Science, Engineering and Technology
A new dynamic clustering approach (DCPSO), based
on Particle Swarm Optimization, is proposed. This approach is
applied to unsupervised image classification. The proposed approach
automatically determines the "optimum" number of clusters and
simultaneously clusters the data set with minimal user interference.
The algorithm starts by partitioning the data set into a relatively large
number of clusters to reduce the effects of initial conditions. Using
binary particle swarm optimization the "best" number of clusters is
selected. The centers of the chosen clusters is then refined via the Kmeans
clustering algorithm. The experiments conducted show that
the proposed approach generally found the "optimum" number of
clusters on the tested images.
Open Science Index 9, 2007