Zaman Serisi Kümeleme Yöntemi ile Küresel Cinsiyet Uçurumu Endeksinin Analizi

Year 2025, Volume: 9 Issue: 1, 420 - 437

Burcu Kartal

https://doi.org/10.29216/ueip.1633127

Abstract

Bu çalışma, dünya ülkelerinin Küresel Cinsiyet Uçurumu Endeksi (Global Gender Gap Index- GGGI) temelinde kümeleme analizini amaçlamaktadır. Bu kapsamda, 2006-2024 yıllarını kapsayan GGGI verileri derlenmiş ve 18 yıllık döneme ait zaman serisi verileri kullanılarak 99 ülkenin kümeleme analizi gerçekleştirilmiştir. Ülkeler arasındaki benzerliklerin belirlenmesi için uzaklık ölçütü olarak Dinamik Zaman Bükmesi (Dynamic Time Warping-DTW) yöntemi kullanılmıştır. Kümeleme analizinde hiyerarşik kümeleme (tek bağlantı, tam bağlantı ve Ward’s yöntemi), k-medoid kümeleme (Partitioning Around Medoids - PAM) ve spektral kümeleme teknikleri uygulanmıştır. Optimum küme sayısının belirlenmesinde ortalama silüet yöntemi kullanılmıştır. Kümeleme performanslarının değerlendirilmesi amacıyla ortalama silüet skoru, Dunn endeksi, Calinski-Harabasz kriteri ve kofenetik korelasyon katsayısı hesaplanmıştır. Analizler sonucunda, dünya ülkelerinin GGGI endeksine dayalı olarak iki temel kümede toplandığı tespit edilmiştir. İlk kümede yer alan ülkelerin GGGI değerlerinde artış eğilimi gözlemlenirken, ikinci kümede yer alan ülkelerin endeks değerlerinde azalan bir trendin olduğu belirlenmiştir.

Keywords

Zaman serisi kümeleme, makine öğrenmesi, küresel cinsiyet farkı endeksi

Analysis of the Global Gender Gap Index Using the Time Series Clustering Method

Abstract

This study aims to cluster analyse the countries of the world based on the Global Gender Gap Index (GGGI). In this context, GGGI data covering the years 2006-2024 were compiled and clustering analysis of 99 countries was carried out using time series data for 18 years. Dynamic Time Warping (DTW) method was used as a distance measure to determine the similarities between countries. In clustering analysis, hierarchical clustering (single linkage, full linkage and Ward's method), k-medoids clustering (Partitioning Around Medoids - PAM) and spectral clustering techniques were applied. The average silhouette method was used to determine the optimum number of clusters. Mean silhouette score, Dunn's index, Calinski-Harabasz criterion and coophenetic correlation coefficient were calculated to evaluate the clustering performances. As a result of the analyses, it was determined that the countries of the world were grouped into two main clusters based on the GGGI index. While an increasing trend is observed in the GGGI values of the countries in the first cluster, it is determined that there is a decreasing trend in the index values of the countries in the second cluster.

Keywords

time series clustering, machine learning, global gender gap index

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