ON FUZZY NEIGHBORHOOD BASED CLUSTERING ALGORITHM WITH LOW COMPLEXITY
Gözde Ulutagay, Efendi N. Nasibov · 2013
Abstract. 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 Modied 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 Modied FJP algorithm both analytically and experimentally. 1.