Algorithms of fuzzy clustering with partial supervision

Authors:
Witold Pedrycz
Affiliations:
Department of Automatic Control and Computer Science, Silesian Technical University, 44-100 Gliwice, Pstrowskiego 16, Poland
Venue:
Pattern Recognition Letters
Year:
1985

Citing 1
Cited 10

Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms

Improving RBF networks performance in regression tasks by means of a supervised fuzzy clustering

Neurocomputing
A knowledge based approach for a fast image retrieval system

Image and Vision Computing
Selection of multiresolution rotationally invariant moments for image recognition

Mathematics and Computers in Simulation
Semi-supervised learning in knowledge discovery

Fuzzy Sets and Systems
Implicitly-supervised learning and its application to fuzzy pattern classifiers

Information Sciences: an International Journal
Partially supervised clustering for image segmentation

Pattern Recognition
A fuzzy minimax clustering model and its applications

Information Sciences: an International Journal
A survey of fuzzy clustering

Mathematical and Computer Modelling: An International Journal
Online fuzzy medoid based clustering algorithms

Neurocomputing
On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification

Neurocomputing

Quantified Score

Hi-index	0.10

Visualization

Abstract

The paper is concerned with methods of fuzzy clustering based minimization of a scalar performance index with the aid of some labelled patterns. Some modifications of the performance index that take into account the results of partial supervision are proposed. The results of the algorithms introduced are exemplified by the use of numerical examples (Gustafson's cross and EKG data set).