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» Hierarchical exploration of large multivariate data sets
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BMCBI
2005
122views more  BMCBI 2005»
13 years 8 months ago
FACT - a framework for the functional interpretation of high-throughput experiments
Background: Interpreting the results of high-throughput experiments, such as those obtained from DNA-microarrays, is an often time-consuming task due to the high number of data-po...
Felix Kokocinski, Nicolas Delhomme, Gunnar Wrobel,...
IJCNN
2006
IEEE
14 years 2 months ago
Divide and Conquer Strategies for MLP Training
— Over time, neural networks have proven to be extremely powerful tools for data exploration with the capability to discover previously unknown dependencies and relationships in ...
Smriti Bhagat, Dipti Deodhare
GECCO
2007
Springer
162views Optimization» more  GECCO 2007»
14 years 2 months ago
A multi-objective approach to discover biclusters in microarray data
The main motivation for using a multi–objective evolutionary algorithm for finding biclusters in gene expression data is motivated by the fact that when looking for biclusters ...
Federico Divina, Jesús S. Aguilar-Ruiz
TOIS
2002
97views more  TOIS 2002»
13 years 8 months ago
Burst tries: a fast, efficient data structure for string keys
Many applications depend on efficient management of large sets of distinct strings in memory. For example, during index construction for text databases a record is held for each d...
Steffen Heinz, Justin Zobel, Hugh E. Williams
DMIN
2006
144views Data Mining» more  DMIN 2006»
13 years 10 months ago
Discovering Assignment Rules in Workforce Schedules Using Data Mining
Discovering hidden patterns in large sets of workforce schedules to gain insight into the potential knowledge in workforce schedules are crucial to better understanding the workfor...
Jihong Yan