An investigation of effects on hierarchical clustering of distance measurements
Yükleniyor...
Tarih
2010
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Selcuk University Research Center of Applied Mathematics
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Clustering is an important tool for a variety of applications in data mining, statistical data analysis, data compression, and vector quantization. The goal of clustering is to group data into clusters such that the similarities among data members within the same cluster are maximal while similarities among data members from different clusters are minimal. There are a number of distance measures that have been used as similarity measures. Distance measures play an important role in cluster analysis. The choice of distance measure is extremely important and should not be taken lightly. In this study, the effects of the distance measurements on hierarchical clustering will be investigated. For this purposes 8 different data sets commonly used in the literature and 2 data sets generated from multivariate normal distribution will be used.
Açıklama
URL: http://sjam.selcuk.edu.tr/sjam/article/view/242
Anahtar Kelimeler
Hierarchical clustering, Hiyerarşik kümeleme, Distance measures, Mesafe ölçümleri, Cophenetic correlation, Cophenetic korelasyon, Mixture distance, Karışım mesafesi, Geometric distance, Geometrik mesafe
Kaynak
Selcuk Journal of Applied Mathematics
WoS Q Değeri
Scopus Q Değeri
Cilt
10
Sayı
Künye
Murat, E., Sakallıoğlu, S. (2010). An investigation of effects on hierarchical clustering of distance measurements. Selcuk Journal of Applied Mathematics, 10, 39-53.
