Agglomerative Learning Algorithms for General Fuzzy Min-Max Neural Network

Authors:
Bogdan Gabrys
Affiliations:
Applied Computational Intelligence Research Unit, Division of Computing and Information Systems, University of Paisley, High Street, Paisley PA1 2BE, Scotland, UK
Venue:
Journal of VLSI Signal Processing Systems
Year:
2002

Citing 7
Cited 4

A Robust Competitive Clustering Algorithm With Applications in Computer Vision

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of Artificial Neural Networks

Fundamentals of Artificial Neural Networks
Pattern Recognition and Neural Networks

Pattern Recognition and Neural Networks
Fuzzy Sets, Neural Networks and Soft Computing

Fuzzy Sets, Neural Networks and Soft Computing
Clustering by competitive agglomeration

Pattern Recognition
Fuzzy Classifier Design

Fuzzy Classifier Design
General fuzzy min-max neural network for clustering and classification

IEEE Transactions on Neural Networks

Learning with Missing or Incomplete Data

ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Semi-supervised classification method for dynamic applications

Fuzzy Sets and Systems
Decision support system for water distribution systems based on neural networks and graphs theory for leakage detection

Expert Systems with Applications: An International Journal
Evaluation of hyperbox neural network learning for classification

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper two agglomerative learning algorithms based on new similarity measures defined for hyperbox fuzzy sets are proposed. They are presented in a context of clustering and classification problems tackled using a general fuzzy min-max (GFMM) neural network. The proposed agglomerative schemes have shown robust behaviour in presence of noise and outliers and insensitivity to the order of training patterns presentation. The emphasis is also put on the complimentary features to the previously presented incremental learning scheme more suitable for on-line adaptation and dealing with large training data sets. The performance and other properties of the agglomerative schemes are illustrated using a number of artificial and real-world data sets.