Posterior probability convergence of k-NN classification and K-means clustering
27h International Symposium on Computer and Information Sciences, ISCIS 2012, Paris, Fransa, 3 - 04 Ekim 2012, ss.171-179, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1007/978-1-4471-4594-3_18
- Basıldığı Şehir: Paris
- Basıldığı Ülke: Fransa
- Sayfa Sayıları: ss.171-179
- Anahtar Kelimeler: Clustering, K-Means, K-Medoids, K-NN classification, Local learning
- Boğaziçi Üniversitesi Adresli: Evet
Özet
Centroid based clustering methods, such as K-Means, form Voronoi cells whose radii are inversely proportional to number of clusters, K, and the expectation of posterior probability distribution in the closest cluster is related to that of a k-Nearest Neighbor Classifier (k-NN) due to the Law of Large Numbers. The aim of this study is to examine the relationship of these two seemingly different concepts of clustering and classification, more specifically, the relationship between k of k-NN and K of K-Means. One specific application area of this correspondence is local learning. The study provides experimental convergence evidence and complexity analysis to address the relative advantages of two methods in local learning applications. © 2013 Springer-Verlag London.