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ON FUZZY NEIGHBORHOOD BASED CLUSTERING ALGORITHM WITH LOW COMPLEXITY

Publish Year: 1392
Type: Journal paper
Language: English
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Document National Code:

JR_IJFS-10-3_002

Index date: 26 June 2022

ON FUZZY NEIGHBORHOOD BASED CLUSTERING ALGORITHM WITH LOW COMPLEXITY abstract

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 Modi ed 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 Modi edFJP algorithm both analytically and experimentally.

ON FUZZY NEIGHBORHOOD BASED CLUSTERING ALGORITHM WITH LOW COMPLEXITY Keywords:

ON FUZZY NEIGHBORHOOD BASED CLUSTERING ALGORITHM WITH LOW COMPLEXITY authors

Gozde Ulutagay

Department of Industrial Engineering, Izmir University, Gursel Aksel Blv ۱۴, Uckuyular, Izmir, Turkey

Efendi Nasibov

Department of Computer Science, Dokuz Eylul University, Izmir, ۳۵۱۶۰, Turkey, Institute of Cybernetics, Azerbaijan National Academy of Sciences, Azerbaijan

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