Document Type : Research Paper


1 Department of Industrial Engineering, Izmir University, Gursel Aksel Blv 14, Uckuyular, Izmir, Turkey

2 Department of Computer Science, Dokuz Eylul University, Izmir, 35160, Turkey, Institute of Cybernetics, Azerbaijan National Academy of Sciences, Azerbaijan


The main purpose of this paper is to achieve improvement in the
speed of Fuzzy Joint Points (FJP) algorithm. Since FJP approach is a basis
for fuzzy neighborhood based clustering algorithms such as Noise-Robust FJP
(NRFJP) and Fuzzy Neighborhood DBSCAN (FN-DBSCAN), improving FJP
algorithm would an important achievement in terms of these FJP-based meth-
ods. Although FJP has many advantages such as robustness, auto detection
of the optimal number of clusters by using cluster validity, independency from
scale, etc., it is a little bit slow. In order to eliminate this disadvantage, by im-
proving the FJP algorithm, we propose a novel Modi ed FJP algorithm, which
theoretically runs approximately n= log2 n times faster and which is less com-
plex than the FJP algorithm. We evaluated the performance of the Modi ed
FJP algorithm both analytically and experimentally.


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