Eda ÖZKUL, Özge TEZEL, Orhan KESEMEN, Buğra Kaan TİRYAKİ

Fuzzy c-Means Directional Clustering (FCMDC) algorithm using trigonometric approximation

Cluster analysis is widely used in data analysis. Statistical data analysis is generally performed on thelinear data. If the data has directional structure, classical statistical methods cannot be applied directly to it. Thisstudy aims to improve a new directional clustering algorithm which is based on trigonometric approximation. Thetrigonometric approximation is used for both descriptive statistics and clustering of directional data. In this paper, thefuzzy clustering algorithms (FCD and FCM4DD) improved for directional data and the proposed method are carried outon some numerical and real data examples, and the simulation results are presented. Consequently, these results indicatethat the fuzzy c-means directional clustering algorithm gives the better results from the points of the mean square errorand the standard deviation for cluster centers.

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