OWA yaklaşımı ile desteklenen K-Centroid bağlantılı güçlendirilmiş hiyerarşik kümeleme bağlantı algoritması

Abstract

Hiyerarşik kümeleme işleminin kalitesini belirlemede bağlantı algoritması önemli bir rol oynamaktadır. Bu nedenle, dikkatli bir şekilde seçilmelidir. Bu seçim, kümeleme sürecinin etkinliğini önemli ölçüde etkilemektedir. Bununla birlikte, geleneksel bağlantı türleri, kümelerin merkez noktalarına yakın olan çevresel kayıtların etkisini dikkate almamaktadır. Bu eksikliği gidermek amacıyla, önceki çalışmalarda k-centroid bağlantı adı verilen yeni bir küme birleştirme kriter modeli önerilmiştir. Bu model, kümeleme kalitesini artırmak için küme merkezlerine yakın örnekleri detaylı bir şekilde analiz etmektedir. K-centroid bağlantı, her küme içerisindeki merkez noktasına en yakın k veri noktasının ortalama uzaklığını hesaplamaktadır. Bu çalışmada, k-centroid bağlantı kümeleme yeteneği Sıralı Ağırlıklı Ortalama (OWA) yaklaşımı ile desteklenerek geliştirilmiştir. Küme merkezlerine en yakın k kayıt arasındaki ortalama mesafelerin OWA değerleri, doğrudan bu mesafeleri hesaplamak yerine farklı α değerleri için eş seviyeli ağırlıklı stres fonksiyonu kullanılarak hesaplanmıştır. Bu yeni model, kümeleme için tasarlanmış 24 farklı açık erişimli veri kümesi üzerinde değerlendirilmiştir. Sonuçlar, farklı stres fonksiyonları için OWA yaklaşımlarının desteğiyle k-centroid bağlantı modelinin önemli ölçüde geliştirilebileceğini göstermektedir.

Keywords

• Makine öğrenimi
• Hiyerarşik kümeleme
• Bağlantı yöntemi
• OWA

A boosted hierarchical clustering linkage algorithm: K-Centroid link supported with OWA approach

Abstract

The choice of linkage algorithm plays a crucial role in determining the quality of hierarchical clustering and therefore must be made carefully. This selection significantly influences the effectiveness of the clustering process. However, conventional linkage methods do not take into account the influence of records located near the cluster centers. Previous studies proposed the k-centroid link, a new cluster merging criterion that analyzes instances near cluster centers in greater detail to improve clustering quality. The k-centroid link computes the average distance among the k nearest data points to the central point within each cluster. In this study, we enhance the clustering capability of the k-centroid link by integrating the Ordered Weighted Averaging (OWA) approach. Specifically, OWA values of the average distances between the k nearest records to each cluster center are calculated using a constant-level weighted stress function across different α values, rather than relying solely on direct distance calculations. The proposed model was evaluated on 24 publicly available benchmark datasets specifically designed for clustering tasks. The results demonstrate that the k-centroid link can be significantly improved through the application of OWA-based approaches with different stress functions.

Keywords

• Machine learning
• Hierarchical clustering
• Linkage method
• OWA

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