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» Parallel k h-Means Clustering for Large Data Sets
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IPPS
2006
IEEE
15 years 3 months ago
Lossless compression for large scale cluster logs
The growing computational and storage needs of several scientific applications mandate the deployment of extreme-scale parallel machines, such as IBM’s Blue Gene/L which can acc...
R. Balakrishnan, Ramendra K. Sahoo
OSDI
2004
ACM
15 years 9 months ago
MapReduce: Simplified Data Processing on Large Clusters
MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to ge...
Jeffrey Dean, Sanjay Ghemawat
BMCBI
2010
139views more  BMCBI 2010»
14 years 9 months ago
A highly efficient multi-core algorithm for clustering extremely large datasets
Background: In recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput t...
Johann M. Kraus, Hans A. Kestler
STACS
2007
Springer
15 years 3 months ago
Small Space Representations for Metric Min-Sum k -Clustering and Their Applications
The min-sum k-clustering problem is to partition a metric space (P, d) into k clusters C1, . . . , Ck ⊆ P such that k i=1 p,q∈Ci d(p, q) is minimized. We show the first effi...
Artur Czumaj, Christian Sohler
TVCG
2010
141views more  TVCG 2010»
14 years 8 months ago
Fast Construction of k-Nearest Neighbor Graphs for Point Clouds
—We present a parallel algorithm for k-nearest neighbor graph construction that uses Morton ordering. Experiments show that our approach has the following advantages over existin...
Michael Connor, Piyush Kumar