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» A Genetic Algorithm for Clustering on Very Large Data Sets
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SBACPAD
2003
IEEE
180views Hardware» more  SBACPAD 2003»
13 years 10 months ago
New Parallel Algorithms for Frequent Itemset Mining in Very Large Databases
Frequent itemset mining is a classic problem in data mining. It is a non-supervised process which concerns in finding frequent patterns (or itemsets) hidden in large volumes of d...
Adriano Veloso, Wagner Meira Jr., Srinivasan Parth...
CIBCB
2008
IEEE
13 years 11 months ago
Very large scale ReliefF for genome-wide association analysis
— The genetic causes of many monogenic diseases have already been discovered. However, most common diseases are actually the result of complex nonlinear interactions between mult...
Margaret J. Eppstein, Paul Haake
IDA
2011
Springer
12 years 11 months ago
A parallel, distributed algorithm for relational frequent pattern discovery from very large data sets
The amount of data produced by ubiquitous computing applications is quickly growing, due to the pervasive presence of small devices endowed with sensing, computing and communicatio...
Annalisa Appice, Michelangelo Ceci, Antonio Turi, ...
KDD
2002
ACM
155views Data Mining» more  KDD 2002»
14 years 5 months ago
SyMP: an efficient clustering approach to identify clusters of arbitrary shapes in large data sets
We propose a new clustering algorithm, called SyMP, which is based on synchronization of pulse-coupled oscillators. SyMP represents each data point by an Integrate-and-Fire oscill...
Hichem Frigui
ICDE
2010
IEEE
491views Database» more  ICDE 2010»
14 years 4 months ago
TrajStore: An Adaptive Storage System for Very Large Trajectory Data Sets
The rise of GPS and broadband-speed wireless devices has led to tremendous excitement about a range of applications broadly characterized as "location based services". Cu...
Philippe Cudré-Mauroux, Eugene Wu, Samuel M...