%0 Journal Article
%T ON FUZZY NEIGHBORHOOD BASED CLUSTERING
ALGORITHM WITH LOW COMPLEXITY
%J Iranian Journal of Fuzzy Systems
%I University of Sistan and Baluchestan
%Z 1735-0654
%A Ulutagay, Gozde
%A Nasibov, Efendi
%D 2013
%\ 06/30/2013
%V 10
%N 3
%P 1-20
%! ON FUZZY NEIGHBORHOOD BASED CLUSTERING
ALGORITHM WITH LOW COMPLEXITY
%K Clustering
%K Fuzzy neighborhood relation
%K Complexity
%K Modied FJP
%R 10.22111/ijfs.2013.806
%X The main purpose of this paper is to achieve improvement in thespeed of Fuzzy Joint Points (FJP) algorithm. Since FJP approach is a basisfor fuzzy neighborhood based clustering algorithms such as Noise-Robust FJP(NRFJP) and Fuzzy Neighborhood DBSCAN (FN-DBSCAN), improving FJPalgorithm would an important achievement in terms of these FJP-based meth-ods. Although FJP has many advantages such as robustness, auto detectionof the optimal number of clusters by using cluster validity, independency fromscale, etc., it is a little bit slow. In order to eliminate this disadvantage, by im-proving the FJP algorithm, we propose a novel Modied FJP algorithm, whichtheoretically runs approximately n= log2 n times faster and which is less com-plex than the FJP algorithm. We evaluated the performance of the ModiedFJP algorithm both analytically and experimentally.
%U https://ijfs.usb.ac.ir/article_806_29d29dbb033397c08126e891f30d1646.pdf