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(14)
Handling very large numbers of association rules in the analysis of microarray data
Arraybased Comparative Genomic Hybridization for GenomeWide Screening of DNA Copy Number in Bladder Tumors1
Representational Oligonucleotide Microarray Analysis: A HighResolution Method to Detect Genome Copy Number Variation
ConstraintBased Mining of Formal Concepts in Transactional Data
Fast Algorithms for Mining Association Rules
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Constraintbased concept mining and its application to microarray data analysis
Constraintbased concept mining and its application to microarray data analysis,Intelligent Data Analysis,Jérémy Besson,Céline Robardet,Jeanfrançois
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Constraintbased concept mining and its application to microarray data analysis
(
Citations: 36
)
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Jérémy Besson
,
Céline Robardet
,
Jeanfrançois Boulicaut
,
Sophie Rome
We are designing new
data mining
techniques on boolean contexts to iden tify a priori interesting bisets, i.e., sets of objects (or transactions) and asso ciated sets of attributes (or items). It improves the state of the art in many application domains where transactional/boolean data are to be mined (e.g., basket analysis, WWW usage mining,
gene expression data
analysis). The socalled (formal) concepts are important special cases of a priori interesting bisets that associate closed sets on both dimensions thanks to the Galois op erators. Concept mining in boolean data is tractable provided that at least one of the dimensions (number of objects or attributes) is small enough and the data is not too dense. The task is extremely hard otherwise. Further more, it is important to enable userdefined constraints on the desired bisets and use them during the extraction to increase both the efficiency and the a priori interestingness of the extracted patterns. It leads us to the design of a new algorithm, called DMiner, for mining concepts under constraints. We provide an
experimental validation
on benchmark data sets. Moreover, we introduce an original
data mining
technique for
microarray data
analy sis. Not only boolean expression properties of genes are recorded but also we add biological information about transcription factors. In such a context, DMiner can be used for concept mining under constraints and outperforms the other studied algorithms. We show also that data enrichment is useful for evaluating the biological relevancy of the extracted concepts.
Journal:
Intelligent Data Analysis  IDA
, vol. 9, no. 1, pp. 5982, 2005
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Citation Context
(26)
...LCM [7] and DMiner [
15
], therefore, we can obtain formal concepts...
Yoshiaki Okubo
,
et al.
An Algorithm for Extracting Rare Concepts with Concise Intents
...Among others, this concerns constraintbased mining of closed patterns from binary relations (see, e. g., [23, 28,
4
, 25, 26])...
Loïc Cerf
,
et al.
Mining Constrained CrossGraph Cliques in Dynamic Networks
...MINER [
6
], a closedset mining algorithm that can take into account both types of constraints, towards SCMINER, which handles IL constraints and enumerates both bins and objects...
...4.1 Candidate generation The core technique used by SCMINER to handle IL constraints is based on the “divideandconquer” generic algorithm proposed in [
6
, 14]...
Élisa Fromont
,
et al.
ConstraintBased Subspace Clustering
...Given a pair of parameters, minsup and minlen, DMiner [
2
] can enumerate all closed itemsets J such that sup(J) ≥ minsup and J ≥ minlen...
Yoshiaki Okubo
,
et al.
Finding TopN Pseudo Formal Concepts with Core Intents
...It is to be noted that closed itemsets are extensively used in bioinformatics [3][
27
][28], web usage mining and association rules...
R. V. Nataraj
,
et al.
A framework for mining topk frequent closed itemsets using order pres...
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(
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Jérémy Besson
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Jeanfrançois Boulicaut
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Sort by:
Citations
(36)
An Algorithm for Extracting Rare Concepts with Concise Intents
(
Citations: 1
)
Yoshiaki Okubo
,
Makoto Haraguchi
Conference:
International Conference on Formal Concept Analysis  ICFCA
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Mining Constrained CrossGraph Cliques in Dynamic Networks
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(
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)
Loïc Cerf
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