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» Analysis of variance for fuzzy data
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CSDA
2010
105views more  CSDA 2010»
13 years 8 months ago
James-Stein shrinkage to improve k-means cluster analysis
We study a general algorithm to improve accuracy in cluster analysis that employs the James-Stein shrinkage effect in k-means clustering. We shrink the centroids of clusters towar...
Jinxin Gao, David B. Hitchcock
ISBI
2007
IEEE
14 years 3 months ago
Statistical Shape Analysis via Principal Factor Analysis
Statistical shape analysis techniques commonly employed in the medical imaging community, such as Active Shape Models or Active Appearance Models, rely on Principal Component Anal...
Mauricio Reyes, Marius George Linguraru, Kostas Ma...
SDM
2011
SIAM
241views Data Mining» more  SDM 2011»
12 years 11 months ago
A Fast Algorithm for Sparse PCA and a New Sparsity Control Criteria
Sparse principal component analysis (PCA) imposes extra constraints or penalty terms to the standard PCA to achieve sparsity. In this paper, we first introduce an efficient algor...
Yunlong He, Renato Monteiro, Haesun Park
IPOM
2007
Springer
14 years 2 months ago
Measurement and Analysis of Intraflow Performance Characteristics of Wireless Traffic
It is by now widely accepted that the arrival process of aggregate network traffic exhibits self-similar characteristics which result in the preservation of traffic burstiness (hig...
Dimitrios P. Pezaros, Manolis Sifalakis, David Hut...
ISBRA
2007
Springer
14 years 2 months ago
GFBA: A Biclustering Algorithm for Discovering Value-Coherent Biclusters
Clustering has been one of the most popular approaches used in gene expression data analysis. A clustering method is typically used to partition genes according to their similarity...
Xubo Fei, Shiyong Lu, Horia F. Pop, Lily R. Liang