Diversity and adaptation in populations of clustering ants
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering by Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Alternatives to the k-means algorithm that find better clusterings
Proceedings of the eleventh international conference on Information and knowledge management
Gene Clustering Using Self-Organizing Maps and Particle Swarm Optimization
IPDPS '03 Proceedings of the 17th International Symposium on Parallel and Distributed Processing
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
On clustering tree structured data with categorical nature
Pattern Recognition
Differential evolution and particle swarm optimisation in partitional clustering
Computational Statistics & Data Analysis
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
Convergence analysis and improvements of quantum-behaved particle swarm optimization
Information Sciences: an International Journal
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
| Hi-index | 0.00 |
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.