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» Evaluation of clustering algorithms for gene expression data
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CIBCB
2005
IEEE
16 years 8 days ago
Feedback Memetic Algorithms for Modeling Gene Regulatory Networks
— In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data. We focus on the evaluation of the performance of memetic algori...
Christian Spieth, Felix Streichert, Jochen Supper,...
CORR
2010
Springer
139views Education» more  CORR 2010»
15 years 6 months ago
Fast Overlapping Group Lasso
The group Lasso is an extension of the Lasso for feature selection on (predefined) non-overlapping groups of features. The non-overlapping group structure limits its applicability...
Jun Liu, Jieping Ye
IDA
2006
Springer
15 years 6 months ago
Backward chaining rule induction
Exploring the vast number of possible feature interactions in domains such as gene expression microarray data is an onerous task. We describe Backward-Chaining Rule Induction (BCR...
Douglas H. Fisher, Mary E. Edgerton, Zhihua Chen, ...
PRL
2006
139views more  PRL 2006»
15 years 6 months ago
Adaptive Hausdorff distances and dynamic clustering of symbolic interval data
This paper presents a partitional dynamic clustering method for interval data based on adaptive Hausdorff distances. Dynamic clustering algorithms are iterative two-step relocatio...
Francisco de A. T. de Carvalho, Renata M. C. R. de...
CIKM
2006
Springer
15 years 8 months ago
Efficiently clustering transactional data with weighted coverage density
In this paper, we propose a fast, memory-efficient, and scalable clustering algorithm for analyzing transactional data. Our approach has three unique features. First, we use the c...
Hua Yan, Keke Chen, Ling Liu