Dominant Color Extraction Based on Dynamic Clustering by Multi-dimensional Particle Swarm Optimization

2009 ◽  
Author(s):  
Serkan Kiranyaz ◽  
Stefan Uhlmann ◽  
Moncef Gabbouj
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Dynamic Clustering ◽  
Swarm Optimization ◽  
Dominant Color

Dynamic clustering using combinatorial particle swarm optimization

2012 ◽  
Vol 38 (3) ◽  
pp. 289-314 ◽  
Author(s):  
Hamid Masoud ◽  
Saeed Jalili ◽  
Seyed Mohammad Hossein Hasheminejad
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Dynamic Clustering ◽  
Swarm Optimization

scholarly journals Perceptual Dominant Color Extraction by Multidimensional Particle Swarm Optimization

2010 ◽  
Vol 2009 (1) ◽  
Author(s):  
Serkan Kiranyaz ◽  
Stefan Uhlmann (EURASIP Member) ◽  
Turker Ince ◽  
Moncef Gabbouj
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Dominant Color

Integration of Particle Swarm Optimization and Immune Genetic Algorithm-Based Dynamic Clustering for Customer Clustering

2015 ◽  
Vol 24 (05) ◽  
pp. 1550019 ◽  
Author(s):  
R. J. Kuo ◽  
S. H. Lin ◽  
Zhen-Yao Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Data Sets ◽  
Dynamic Clustering ◽  
Immune Genetic Algorithm ◽  
Swarm Optimization ◽  
Benchmark Data ◽  
Real World Problem ◽  
Wine Glass ◽  
Stability And Accuracy

This study intends to present a dynamic clustering (DC) approach based on particle swarm optimization (PSO) and immune genetic (IG) (DCPIG) algorithm, which is able to cluster the data into adequate clusters through data characteristics with pre-specified numbers of clusters. The proposed DCPIG algorithm is compared with three DC algorithms in the literature using Iris, Wine, Glass and Vowel benchmark data sets. The experiment results show that the DCPIG algorithm can achieve higher stability and accuracy than the other algorithms. In addition, the DCPIG algorithm is also applied to a real-world problem considering the customer clustering for a cyber flower shop. Lastly, we recommend different products and services to customers based on the clustering results.

Quantum-Behaved Particle Swarm Optimization Dynamic Clustering Algorithm

2013 ◽  
Vol 694-697 ◽  
pp. 2757-2760 ◽  
Author(s):  
Chun Yan Zhang ◽  
Wei Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Clustering Algorithm ◽  
Learning Strategy ◽  
Clustering Algorithms ◽  
Particle Swarm ◽  
Optimal Number ◽  
Data Sets ◽  
Dynamic Clustering ◽  
Swarm Optimization ◽  
Coding Method

This paper proposed a revised quantum-behaved particle swarm optimization algorithm utilizing comprehensive learning strategy to prevent the universal tendency of premature convergence, based on which introduced a novel data clustering algorithm as well. The optimal number of cluster could be automatically obtained by this novel clustering algorithm because a new special coding method for particles was used. Compared with another two dynamic clustering algorithms on five testing data sets, the proposed dynamic clustering algorithm based on the comprehensive learning strategy has the best performance and with the best potential application prospect.

Sign in / Sign up

Export Citation Format

Share Document