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181
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ICPP
2000
IEEE
15 years 9 months ago
A Scalable Parallel Subspace Clustering Algorithm for Massive Data Sets
Clustering is a data mining problem which finds dense regions in a sparse multi-dimensional data set. The attribute values and ranges of these regions characterize the clusters. ...
Harsha S. Nagesh, Sanjay Goil, Alok N. Choudhary
IPPS
1997
IEEE
15 years 8 months ago
Control Schemes in a Generalized Utility for Parallel Branch-and-Bound Algorithms
Branch-and-bound algorithms are general methods applicable to various combinatorial optimization problems and parallelization is one of the most hopeful methods to improve these a...
Yuji Shinano, Kenichi Harada, Ryuichi Hirabayashi
161
Voted
BMCBI
2010
153views more  BMCBI 2010»
15 years 4 months ago
Pash 3.0: A versatile software package for read mapping and integrative analysis of genomic and epigenomic variation using massi
Background: Massively parallel sequencing readouts of epigenomic assays are enabling integrative genome-wide analyses of genomic and epigenomic variation. Pash 3.0 performs sequen...
Cristian Coarfa, Fuli Yu, Christopher A. Miller, Z...
175
Voted
PVLDB
2008
182views more  PVLDB 2008»
15 years 4 months ago
SCOPE: easy and efficient parallel processing of massive data sets
Companies providing cloud-scale services have an increasing need to store and analyze massive data sets such as search logs and click streams. For cost and performance reasons, pr...
Ronnie Chaiken, Bob Jenkins, Per-Åke Larson,...
140
Voted
KDD
2005
ACM
170views Data Mining» more  KDD 2005»
16 years 5 months ago
Parallel mining of closed sequential patterns
Discovery of sequential patterns is an essential data mining task with broad applications. Among several variations of sequential patterns, closed sequential pattern is the most u...
Shengnan Cong, Jiawei Han, David A. Padua