Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning - Special issue on learning with probabilistic representations
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Machine Learning
Machine Learning
Algorithmic Learning in a Random World
Algorithmic Learning in a Random World
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Combining neural networks and semantic feature space for email classification
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Multi-objective rule mining using a chaotic particle swarm optimization algorithm
Knowledge-Based Systems
Semi-supervised fuzzy clustering: A kernel-based approach
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
A support vector machine-based model for detecting top management fraud
Knowledge-Based Systems
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Multi-objective hybrid evolutionary algorithms for radial basis function neural network design
Knowledge-Based Systems
WSEAS Transactions on Information Science and Applications
Game team balancing by using particle swarm optimization
Knowledge-Based Systems
A new method to determine basic probability assignment from training data
Knowledge-Based Systems
A sample-based hierarchical adaptive K-means clustering method for large-scale video retrieval
Knowledge-Based Systems
Hi-index | 0.00 |
This paper introduces a new hybrid cluster validity method based on particle swarm optimization, for successfully solving one of the most popular clustering/classifying complex datasets problems. The proposed method for the solution of the clustering/classifying problem, designated as PSORS index method, combines a particle swarm optimization (PSO) algorithm, Rough Set (RS) theory and a modified form of the Huang index function. In contrast to the Huang index method which simply assigns a constant number of clusters to each attribute, this method could cluster the values of the individual attributes within the dataset and achieves both the optimal number of clusters and the optimal classification accuracy. The validity of the proposed approach is investigated by comparing the classification results obtained for a real-world dataset with those obtained by pseudo-supervised classification BPNN, decision-tree and Huang index methods. There is good evidence to show that the proposed PSORS index method not only has a superior clustering accomplishment than the considered methods, but also achieves better classification accuracy.