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» Classes and clusters in data analysis
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CSDA
2010
105views more  CSDA 2010»
15 years 4 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»
15 years 5 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 11 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»
15 years 3 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»
15 years 4 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