ARKLI BAĞLANTI YÖNTEMLERİ İLE HİYERARŞİK KÜMELEME TOPLULUĞU

Kümeleme topluluğu, yüksek kümeleme performansı sağlaması nedeniyle son yıllarda tercih edilen bir teknik haline gelmiştir. Bu çalışmada, Bağlantı-tabanlı Hiyerarşik Kümeleme Topluluğu (BHKT) olarak isimlendirilen yeni bir yaklaşım önerilmektedir. Önerilen yaklaşımda, topluluk elemanları farklı bağlantı yöntemleri kullanarak hiyerarşik kümeleme yapmakta ve sonrasında çoğunluk oylaması ile ortak karar üretmektedir. Çalışmada kullanılan bağlantı yöntemleri: tek bağlantı, tam bağlantı, ortalama bağlantı, merkez bağlantı, Ward yöntemi, komşu birleştirme yöntemi ve ayarlı tam bağlantıdır. Ayrıca çalışmada, farklı boyutlardaki hiyerarşik kümeleme toplulukları incelenmiş ve birbiriyle karşılaştırılmıştır. Deneysel çalışmalarda, hiyerarşik kümeleme toplulukları 8 farklı veri setinde uygulanmış ve tek bir kümeleme algoritmasına göre daha iyi sonuçlar elde edilmiştir.

Hierarchical Clustering Ensemble with Different Linkage Methods

Clustering ensemble has become a preferred technique in recent years due to the high clustering performance it provides. In this study, a new approach called Link-based Hierarchical Clustering Ensemble (LHCE) is proposed. In the proposed approach, the ensemble members perform hierarchical clustering using different linkage methods and then make joint decisions with majority voting. Linkage methods used in this study are single linkage, complete linkage, average linkage, centroid linkage, Ward method, neighbor joining and adjusted complete linkage. In this study, hierarchical clustering ensembles with different sizes were also investigated and compared with each other. In the experimental studies, hierarchical clustering ensembles were applied on 8 different datasets and better results were obtained rather than a single clustering algorithm.

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