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» Comparing Massive High-Dimensional Data Sets
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CIKM
2006
Springer
13 years 11 months ago
Finding highly correlated pairs efficiently with powerful pruning
We consider the problem of finding highly correlated pairs in a large data set. That is, given a threshold not too small, we wish to report all the pairs of items (or binary attri...
Jian Zhang, Joan Feigenbaum
SDM
2011
SIAM
414views Data Mining» more  SDM 2011»
12 years 10 months ago
Clustered low rank approximation of graphs in information science applications
In this paper we present a fast and accurate procedure called clustered low rank matrix approximation for massive graphs. The procedure involves a fast clustering of the graph and...
Berkant Savas, Inderjit S. Dhillon
KDD
2005
ACM
153views Data Mining» more  KDD 2005»
14 years 7 months ago
Using retrieval measures to assess similarity in mining dynamic web clickstreams
While scalable data mining methods are expected to cope with massive Web data, coping with evolving trends in noisy data in a continuous fashion, and without any unnecessary stopp...
Olfa Nasraoui, Cesar Cardona, Carlos Rojas
BMCBI
2007
128views more  BMCBI 2007»
13 years 7 months ago
Compression-based classification of biological sequences and structures via the Universal Similarity Metric: experimental assess
Background: Similarity of sequences is a key mathematical notion for Classification and Phylogenetic studies in Biology. It is currently primarily handled using alignments. Howeve...
Paolo Ferragina, Raffaele Giancarlo, Valentina Gre...
ISBI
2008
IEEE
14 years 8 months ago
Improved fMRI group studies based on spatially varying non-parametric BOLD signal modeling
Multi-subject analysis of functional Magnetic Resonance Imaging (fMRI) data relies on within-subject studies, which are usually conducted using a massively univariate approach. In...
Philippe Ciuciu, Thomas Vincent, Anne-Laure Fouque...