کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
531260 | 869821 | 2006 | 8 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Clustering noisy data in a reduced dimension space via multivariate regression trees Clustering noisy data in a reduced dimension space via multivariate regression trees](/preview/png/531260.png)
Cluster analysis is sensitive to noise variables intrinsically contained within high dimensional data sets. As the size of data sets increases, clustering techniques robust to noise variables must be identified. This investigation gauges the capabilities of recent clustering algorithms applied to two real data sets increasingly perturbed by superfluous noise variables. The recent techniques include mixture models of factor analysers and auto-associative multivariate regression trees. Statistical techniques are integrated to create two approaches useful for clustering noisy data: multivariate regression trees with principal component scores and multivariate regression trees with factor scores. The tree techniques generate the superior clustering results.
Journal: Pattern Recognition - Volume 39, Issue 3, March 2006, Pages 424–431