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ICML
2004
IEEE
14 years 11 months ago
K-means clustering via principal component analysis
Principal component analysis (PCA) is a widely used statistical technique for unsupervised dimension reduction. K-means clustering is a commonly used data clustering for unsupervi...
Chris H. Q. Ding, Xiaofeng He
BIBE
2007
IEEE
155views Bioinformatics» more  BIBE 2007»
14 years 4 months ago
Partial Mixture Model for Tight Clustering in Exploratory Gene Expression Analysis
Abstract—In this paper we demonstrate the inherent robustness of minimum distance estimator that makes it a potentially powerful tool for parameter estimation in gene expression ...
Yinyin Yuan, Chang-Tsun Li
IWCC
1999
IEEE
14 years 2 months ago
Design and Analysis of the Alliance/University of New Mexico Roadrunner Linux SMP SuperCluster
This paper will discuss high performance clustering from a series of critical topics: architectural design, system software infrastructure, and programming environment. This will ...
David A. Bader, Arthur B. Maccabe, Jason R. Mastal...
BMCBI
2011
13 years 5 months ago
A novel approach to the clustering of microarray data via nonparametric density estimation
Background: Cluster analysis is a crucial tool in several biological and medical studies dealing with microarray data. Such studies pose challenging statistical problems due to di...
Riccardo De Bin, Davide Risso
SODA
2010
ACM
171views Algorithms» more  SODA 2010»
13 years 8 months ago
Differential Privacy in New Settings
Differential privacy is a recent notion of privacy tailored to the problem of statistical disclosure control: how to release statistical information about a set of people without ...
Cynthia Dwork