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» Classes and clusters in data analysis
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CSDA
2010
105views more  CSDA 2010»
14 years 10 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
SDM
2007
SIAM
98views Data Mining» more  SDM 2007»
14 years 11 months ago
Lattice based Clustering of Temporal Gene-Expression Matrices
Individuals show different cell classes when they are in the different stages of a disease, have different disease subtypes, or have different response to a treatment or envir...
Yang Huang, Martin Farach-Colton
CVPR
2009
IEEE
16 years 5 months ago
Robust Multi-Class Transductive Learning with Graphs
Graph-based methods form a main category of semisupervised learning, offering flexibility and easy implementation in many applications. However, the performance of these methods...
Wei Liu (Columbia University), Shih-fu Chang (Colu...
PR
2007
139views more  PR 2007»
14 years 9 months ago
Learning the kernel matrix by maximizing a KFD-based class separability criterion
The advantage of a kernel method often depends critically on a proper choice of the kernel function. A promising approach is to learn the kernel from data automatically. In this p...
Dit-Yan Yeung, Hong Chang, Guang Dai
CORR
2002
Springer
91views Education» more  CORR 2002»
14 years 9 months ago
Data Engineering for the Analysis of Semiconductor Manufacturing Data
We have analyzed manufacturing data from several different semiconductor manufacturing plants, using decision tree induction software called Q-YIELD. The software generates rules ...
Peter D. Turney