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» Anytime Exploratory Data Analysis for Massive Data Sets
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CIVR
2009
Springer
583views Image Analysis» more  CIVR 2009»
14 years 8 months ago
Mining from Large Image Sets
So far, most image mining was based on interactive querying. Although such querying will remain important in the future, several applications need image mining at such wide scale...
Luc J. Van Gool, Michael D. Breitenstein, Stephan ...
DATAMINE
2006
176views more  DATAMINE 2006»
13 years 8 months ago
A Bit Level Representation for Time Series Data Mining with Shape Based Similarity
Clipping is the process of transforming a real valued series into a sequence of bits representing whether each data is above or below the average. In this paper, we argue that clip...
Anthony J. Bagnall, Chotirat (Ann) Ratanamahatana,...
BMCBI
2002
188views more  BMCBI 2002»
13 years 7 months ago
The limit fold change model: A practical approach for selecting differentially expressed genes from microarray data
Background: The biomedical community is developing new methods of data analysis to more efficiently process the massive data sets produced by microarray experiments. Systematic an...
David M. Mutch, Alvin Berger, Robert Mansourian, A...
NIPS
2003
13 years 9 months ago
Fast Algorithms for Large-State-Space HMMs with Applications to Web Usage Analysis
In applying Hidden Markov Models to the analysis of massive data streams, it is often necessary to use an artificially reduced set of states; this is due in large part to the fac...
Pedro F. Felzenszwalb, Daniel P. Huttenlocher, Jon...
BMCBI
2010
147views more  BMCBI 2010»
13 years 8 months ago
baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data
Background: High throughput sequencing has become an important technology for studying expression levels in many types of genomic, and particularly transcriptomic, data. One key w...
Thomas J. Hardcastle, Krystyna A. Kelly