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KDD
2004
ACM
190views Data Mining» more  KDD 2004»
16 years 4 months ago
Kernel k-means: spectral clustering and normalized cuts
Kernel k-means and spectral clustering have both been used to identify clusters that are non-linearly separable in input space. Despite significant research, these methods have re...
Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis
EUROSYS
2007
ACM
16 years 1 months ago
Dryad: distributed data-parallel programs from sequential building blocks
Dryad is a general-purpose distributed execution engine for coarse-grain data-parallel applications. A Dryad application combines computational “vertices” with communication ...
Michael Isard, Mihai Budiu, Yuan Yu, Andrew Birrel...
ISBI
2011
IEEE
14 years 8 months ago
Atlas-based fiber clustering for multi-subject analysis of high angular resolution diffusion imaging tractography
High angular resolution diffusion imaging (HARDI) allows in vivo analysis of the white matter structure and connectivity. Based on orientation distribution functions (ODFs) that r...
Gautam Prasad, Neda Jahanshad, Iman Aganj, Christo...
CCGRID
2001
IEEE
15 years 8 months ago
Cluster Computing in the Classroom: Topics, Guidelines, and Experiences
- With the progress of research on cluster computing, more and more universities have begun to offer various courses covering cluster computing. A wide variety of content can be ta...
Amy W. Apon, Rajkumar Buyya, Hai Jin, Jens Mache
BMCBI
2006
134views more  BMCBI 2006»
15 years 4 months ago
An approach for clustering gene expression data with error information
Background: Clustering of gene expression patterns is a well-studied technique for elucidating trends across large numbers of transcripts and for identifying likely co-regulated g...
Brian Tjaden