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Scalable Techniques for Mining Causal Structures
Scalable Techniques for Mining Causal Structures,Data Mining and Knowledge Discovery,Craig Silverstein,Sergey Brin,Rajeev Motwani,Jeffrey D. Ullman
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Scalable Techniques for Mining Causal Structures
(
Citations: 34
)
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Craig Silverstein
,
Sergey Brin
,
Rajeev Motwani
,
Jeffrey D. Ullman
Journal:
Data Mining and Knowledge Discovery  DATAMINE
, vol. 4, no. 2/3, pp. 163192, 2000
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Citation Context
(17)
...Algorithms for learning association rules evaluate whether new items are unexpectedly correlated with a target item conditioned on the existing items in the rule [
3
]...
Matthew J. H. Rattigan
,
et al.
Leveraging DSeparation for Relational Data Sets
...“In our view, inferring complete causal models (i.e., causal Bayesian networks) is essentially impossible in largescale data mining applications with thousands of variables” (
Silverstein et al., 2000
)...
Ioannis Tsamardinos
,
et al.
The maxmin hillclimbing Bayesian network structure learning algorith...
...Recent developments in causal inference or causal statistics makes the assignment of cause and effect possible, if the third variab le is available and information on conditional correlation can be obtained (Cooper, 1997; Spirtes et al., 2000; Pearl, 2000;
Silverstein et al., 2000
)...
...The LCD rule is extended in (
Silverstein et al., 2000
) by requiring that x and z are correlated unconditional on y (but uncorrelated conditional on antiCCP RF SE No. samples + + + 960 + +  128 +  + 84 +   19  + + 95  +  74   + 214    149...
...Note that the CCC rule in (
Silverstein et al., 2000
) that does not allow for hidden variables is not discussed here, since the assumption for it s use is violated in our example...
...This is the socalled CCU rule discussed in (
Silverstein et al., 2000
)...
...We have successfully applied a local causality discovery rule (Cooper, 1997;
Silverstein et al., 2000
) to the threevariable set of two biomarkers for rheumatoid arthritis, anticyclic ci trullinated peptide antibody and rheumatoid factor, and one genotype known to be associated with the rheumatoid arthritis, the HLADRB1 allele...
Wentian Li
,
et al.
Inferring causal relationships among intermediate phenotypes and bioma...
...A Local Causal Discovery (LCD [11]) algorithm ([
8
]) is used to study how causal structures can be determined from association rules and generate rules to map symptoms to diseases...
Shibendra S. Pobi
,
et al.
Predicting Juvenile Diabetes from Clinical Test Results
...Following the framework of [
7
], we assume that our data was generated by a causal directed acyclic graph, where an edge A → B has the meaning that “A is a direct cause of B”. There are several advantages on trying to extract subgraphs of the original graph as a type of association rule, instead of discovering a full graph [7], as further discussed in Section 2.1...
...Following the framework of [7], we assume that our data was generated by a causal directed acyclic graph, where an edge A → B has the meaning that “A is a direct cause of B”. There are several advantages on trying to extract subgraphs of the original graph as a type of association rule, instead of discovering a full graph [
7
], as further discussed in Section 2.1...
...What do we gain by extracting association rules from a graphical model instead of trying to learn the graphical structure directly? One major reason is scalability, as motivated by [
7
]: the data might have been generated by a directed graph that is too large to be efficiently learned from data...
Ricardo Silva
,
et al.
Towards Association Rules with Hidden Variables
References
(2)
Bayesian Networks for Data Mining
(
Citations: 186
)
David Heckerman
Journal:
Data Mining and Knowledge Discovery  DATAMINE
, vol. 1, no. 1, pp. 79119, 1997
A Bayesian method for the induction of probabilistic networks fromdata
(
Citations: 1551
)
Gregory F. Cooper
,
Edward Herskovits
Journal:
Machine Learning  ML
, vol. 9, no. 4, pp. 309347, 1992
Sort by:
Citations
(34)
A rough set approach to multiple dataset analysis
(
Citations: 2
)
Ken Kaneiwa
Journal:
Applied Soft Computing  ASC
, vol. 11, no. 2, pp. 25382547, 2011
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation
(
Citations: 16
)
Constantin F. Aliferis
,
Alexander R. Statnikov
,
Ioannis Tsamardinos
,
Subramani Mani
,
Xenofon D. Koutsoukos
Journal:
Journal of Machine Learning Research  JMLR
, vol. 11, pp. 171234, 2010
Leveraging DSeparation for Relational Data Sets
Matthew J. H. Rattigan
,
David Jensen
Conference:
IEEE International Conference on Data Mining  ICDM
, pp. 989994, 2010
Genetic Algorithm Based Bayesian Network for Customers' Behavior Analysis
Xiao Yi Yu
,
Aiming Wang
Published in 2010.
Outofcore coherent closed quasiclique mining from large dense graph databases
(
Citations: 15
)
Zhiping Zeng
,
Jianyong Wang
,
Lizhu Zhou
,
George Karypis
Journal:
ACM Transactions on Database Systems  TODS
, vol. 32, no. 2, pp. 13es, 2007