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SSDBM
2008
IEEE
114views Database» more  SSDBM 2008»
16 years 16 days ago
A General Framework for Increasing the Robustness of PCA-Based Correlation Clustering Algorithms
Abstract. Most correlation clustering algorithms rely on principal component analysis (PCA) as a correlation analysis tool. The correlation of each cluster is learned by applying P...
Hans-Peter Kriegel, Peer Kröger, Erich Schube...
TKDE
2008
152views more  TKDE 2008»
15 years 6 months ago
SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis
Linear Discriminant Analysis (LDA) has been a popular method for extracting features that preserves class separability. The projection functions of LDA are commonly obtained by max...
Deng Cai, Xiaofei He, Jiawei Han
ICSM
1998
IEEE
15 years 10 months ago
Analysis of Programs with Exception-Handling Constructs
Analysis techniques, such as control-flow, data-flow, and control-dependence, are used for a variety of maintenance tasks, including regression testing, dynamic execution profilin...
Saurabh Sinha, Mary Jean Harrold
ICASSP
2009
IEEE
16 years 27 days ago
Exploring functional connectivity in fMRI via clustering
In this paper we investigate the use of data driven clustering methods for functional connectivity analysis in fMRI. In particular, we consider the K-Means and Spectral Clustering...
Archana Venkataraman, Koene R. A. Van Dijk, Randy ...
SIGMOD
2008
ACM
107views Database» more  SIGMOD 2008»
16 years 6 months ago
Outlier-robust clustering using independent components
How can we efficiently find a clustering, i.e. a concise description of the cluster structure, of a given data set which contains an unknown number of clusters of different shape ...
Christian Böhm, Christos Faloutsos, Claudia P...