Clustering Categorical Data Using Hierarchies (CLUCDUH)
Küçük Resim Yok
Tarih
2009
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Clustering large populations is an important problem when the data contain noise and different shapes. A good clustering algorithm or approach should be efficient enough to detect clusters sensitively. Besides space complexity, time complexity also gains importance as the size grows. Using hierarchies we developed a new algorithm to split attributes according to the values they have and choosing the dimension for splitting so as to divide the database roughly into equal parts as much as possible. At each node we calculate some certain descriptive statistical features of the data which reside and by pruning we generate the natural clusters with a complexity of O(n).
Açıklama
Anahtar Kelimeler
Clustering, Entropy, Gini, Pruning, Split, Tree
Kaynak
World Academy of Science, Engineering and Technology
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
56
