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

Authors

ORHAN KESEMEN
ÖZGE TEZEL
EDA ÖZKUL
BUĞRA KAAN TİRYAKİ

DOI

10.3906/elk-1903-118

Abstract

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

Keywords

Directional data, fuzzy directional clustering, trigonometric mean, angular distance

First Page

140

Last Page

152

Recommended Citation

KESEMEN, ORHAN; TEZEL, ÖZGE; ÖZKUL, EDA; and TİRYAKİ, BUĞRA KAAN (2020) "Fuzzy c-Means Directional Clustering (FCMDC) algorithm using trigonometric approximation," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 28: No. 1, Article 10. https://doi.org/10.3906/elk-1903-118
Available at: https://journals.tubitak.gov.tr/elektrik/vol28/iss1/10