A robust algorithm for solution of the fuzzy clustering problem on the basis of the fuzzy joint points method

Authors:
E. N. Nasibov
Affiliations:
Cybernetics Institute of ANAS, Baku, Azerbaijan and Dokuz Eylul University, Izmir, Turkey
Venue:
Cybernetics and Systems Analysis
Year:
2008

Citing 8
Cited 1

Fuzzy sets and their application to clustering and training

Fuzzy sets and their application to clustering and training
Fuzzy clustering based on k-nearest-neighbours rule

Fuzzy Sets and Systems - Special issue on clustering and learning
Identification of States of Complex Systems with Estimation of Admissible Measurement Errors on the Basis of Fuzzy Information

Cybernetics and Systems Analysis
Unsupervised fuzzy clustering with multi-center clusters

Fuzzy Sets and Systems - Clustering and modeling
Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques
An investigation of mountain method clustering for large data sets

Pattern Recognition
Validity-guided (re)clustering with applications to image segmentation

IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model

IEEE Transactions on Fuzzy Systems

Robustness of density-based clustering methods with various neighborhood relations

Fuzzy Sets and Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

The approach called the method of Fuzzy Joint Points (FJP) is considered in which the fuzziness of clusterization lies in the detailedness of taking into account properties of elements in forming sets of similar elements. Based on this approach, a new robust variant of the FJP algorithm is proposed. The properties of this FJP algorithm are analyzed and a sufficient condition for the correct recognition of the hidden structure of clusters is proved.