An autonomous approach to the mountain-clustering method

Author

Branco, P. J Costa ; Lori, N. ; Dente, J.A.

Author_Institution

Lab. de Mecatronica, Inst. Superior Tecnico, Lisbon, Portugal

fYear

1995

Firstpage

649

Lastpage

654

Abstract

This paper presents an autonomous approach to the clustering algorithm based on a mountain function proposed by Yager and Filev (1994). It intends to answer the parameter selection problem and attenuate the effects of the granularity of the griding in algorithm´s performance using a cluster reallocation procedure. The solving of those problems has greatly enhanced the possibility of achieving an autonomous mountain-clustering process. The proposed clustering approach is explained in detail and examples of its performance are analyzed

Keywords

data structures; fuzzy set theory; pattern recognition; autonomous approach; cluster reallocation procedure; fuzzy models; granularity; mountain function; mountain-clustering method; parameter selection problem; problem solving; Buildings; Clustering algorithms; Data structures; Data visualization; Density functional theory; Euclidean distance; Fuzzy sets; Laboratories; Partitioning algorithms; Performance analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Uncertainty Modeling and Analysis, 1995, and Annual Conference of the North American Fuzzy Information Processing Society. Proceedings of ISUMA - NAFIPS '95., Third International Symposium on

Conference_Location

College Park, MD

Print_ISBN

0-8186-7126-2

Type

conf

DOI

10.1109/ISUMA.1995.527771

Filename

527771