@comment{{Bibliography by Michael Hahsler (http://wwwai.wu-wien.ac.at/~hahsler)}}
@comment{{This file has been generated by bib2bib 1.93}}
@comment{{Command line: /usr/bin/bib2bib -c 'category : "measure"' -ob tmp.bib association_rules_url.bib}}
@inproceedings{arules:Aggarwal:1998,
author = {C. C. Aggarwal and P. S. Yu},
title = {A New Framework For Itemset Generation},
booktitle = {PODS 98, Symposium on Principles of Database Systems},
year = {1998},
pages = {18--24},
address = {Seattle, WA, USA},
abstract = {Points out weaknesses of the large frequent itemset method using support
(spuriousness, dense datasets) and that lift gives only values close
to one for items which are very frequent, even if they are perfectly
positive correlated. COLLECTIVE STRENGTH is introduced. Collective
strength uses the violation rate for an itemset which is the fraction
of transactions which contains some, but not all items of the itemset.
The violation rate is compared to the expected violation rate under
independence. Collective strength is downward closed.},
category = {measure},
google = {http://www.google.com/search?q=%22A+New+Framework+For+Itemset+Generation%22},
googlescholar = {http://scholar.google.com/scholar?q=%22A+New+Framework+For+Itemset+Generation%22}
}
@article{arules:Barber:2003,
author = {Brock Barber and Howard J. Hamilton},
title = {Extracting share frequent itemsets with infrequent subsets},
journal = {Data Mining and Knowledge Discovery},
year = {2003},
volume = {7},
pages = {153--185},
abstract = {ITEMSET SHARE is the fraction of some measure (e.g., sales, profit)
contributed by the items in the set. A itemset is share frequent
if it exceeds a threshold. Share frequency is not downward closed!
The article presents several algorithms and heuristics to mine share
frequent itemsets.},
category = {measure},
google = {http://www.google.com/search?q=%22Extracting+share+frequent+itemsets+with+infrequent+subsets%22},
googlescholar = {http://scholar.google.com/scholar?q=%22Extracting+share+frequent+itemsets+with+infrequent+subsets%22}
}
@inproceedings{arules:Blanchard:2005,
author = {Julien Blanchard and Fabrice Guillet and Henri Briand and Regis Gras},
title = {Assessing rule interestingness with a probabilistic measure of deviation from equilibrium},
booktitle = {Proceedings of the 11th international symposium on Applied Stochastic
Models and Data Analysis ASMDA-2005},
year = {2005},
pages = {191--200},
publisher = {ENST},
abstract = {Presents a statistical test for the deviation from the equilibrium
of a rule. The equilibrium for rule a -> b is defined as: the number
of transactions which contain a and b together is equal to the number
of transactions which contain a and not b.},
category = {measure},
google = {http://www.google.com/search?q=%22Assessing+rule+interestingness+with+a+probabilistic+measure+of+deviation+from+equilibrium%22},
googlescholar = {http://scholar.google.com/scholar?q=%22Assessing+rule+interestingness+with+a+probabilistic+measure+of+deviation+from+equilibrium%22}
}
@inproceedings{arules:Brin:1997,
author = {Sergey Brin and Rajeev Motwani and Jeffrey D. Ullman and Shalom Tsur},
title = {Dynamic Itemset Counting and Implication Rules for Market Basket Data},
booktitle = {SIGMOD 1997, Proceedings ACM SIGMOD International Conference on Management
of Data},
year = {1997},
pages = {255--264},
address = {Tucson, Arizona, USA},
month = {May},
abstract = {Introduces CONVICTION (as an improvement to confidence based on implication
rules) and INTEREST (later called LIFT).},
category = {measure},
google = {http://www.google.com/search?q=%22Dynamic+Itemset+Counting+and+Implication+Rules+for+Market+Basket+Data%22},
googlescholar = {http://scholar.google.com/scholar?q=%22Dynamic+Itemset+Counting+and+Implication+Rules+for+Market+Basket+Data%22}
}
@article{arules:Bruzzese:2001,
author = {Dario Bruzzese and Cristina Davino},
title = {Pruning of Discovered Association Rules},
journal = {Computational Statistics},
year = {2001},
volume = {16},
pages = {387--398},
abstract = {The authors construct several statistical tests to evaluate the significance
of discovered associations.},
category = {measure},
publisher = {Physica-Verlag},
google = {http://www.google.com/search?q=%22Pruning+of+Discovered+Association+Rules%22},
googlescholar = {http://scholar.google.com/scholar?q=%22Pruning+of+Discovered+Association+Rules%22}
}
@article{Hahsler:2007,
author = {Michael Hahsler and Kurt Hornik},
title = {New probabilistic interest measures for association rules},
journal = {Intelligent Data Analysis},
year = {2007},
volume = {11},
pages = {437--455},
number = {5},
abstract = {Presents a simple probabilistic framework for transaction data which
can be used to simulate transaction data when no associations are
present. Uses such data and a real-world grocery database to explore
the behavior of confidence and lift, two popular interest measures
used for rule mining. Also introduces the new probabilistic measures
hyper-lift and hyper-confidence.},
booktitle = {From Data and Information Analysis to Knowledge Engineering},
category = {evaluation, measure},
editor = {M. Spiliopoulou and R. Kruse and C. Borgelt and A. N{\"u}rnberger
and W. Gaul},
publisher = {Springer-Verlag},
series = {Studies in Classification, Data Analysis, and Knowledge Organization},
google = {http://www.google.com/search?q=%22New+probabilistic+interest+measures+for+association+rules%22},
googlescholar = {http://scholar.google.com/scholar?q=%22New+probabilistic+interest+measures+for+association+rules%22}
}
@article{arules:Li:2006,
author = {Jiuyong Li},
title = {On Optimal Rule Discovery},
journal = {IEEE Transactions on Knowledge and Data Engineering},
year = {2006},
volume = {18},
pages = {460--471},
number = {4},
abstract = {An optimal rule set (with respect to a metric of interestingness)
contains all rules except those with no greater interestingness than
one of its more general rules. An optimal rule set is a subset of
a nonredundant rule set. The autors present an algorithm called ORD
to find an optimal rule set. Classifiers build on optimal class association
rules are at least as accurate as those built from CBA and C4.5 rule.},
address = {Piscataway, NJ, USA},
category = {measures,classification},
issn = {1041-4347},
publisher = {IEEE Educational Activities Department},
google = {http://www.google.com/search?q=%22On+Optimal+Rule+Discovery%22},
googlescholar = {http://scholar.google.com/scholar?q=%22On+Optimal+Rule+Discovery%22}
}
@inproceedings{arules:Liu:1999b,
author = {Bing Liu and Wynne Hsu and Yiming Ma},
title = {Pruning and summarizing the discovered associations},
booktitle = {Proceedings of the fifth ACM SIGKDD international conference on Knowledge
discovery and data mining (KDD-99)},
year = {1999},
pages = {125--134},
publisher = {ACM Press},
abstract = {Remove insignificant rules using the chi-square test to test for correlation
between the antecedent and the confident of a rule. Also DIRECTION
SETTING (DS) RULES are introduced. A DS rule has a pos. correlated
antecedent and consequent and is not built from a rule with a shorter
antecedent which is a DS rule. Normally, only a small and concise
fraction of rules are DS rules.},
category = {measures,theory},
google = {http://www.google.com/search?q=%22Pruning+and+summarizing+the+discovered+associations%22},
googlescholar = {http://scholar.google.com/scholar?q=%22Pruning+and+summarizing+the+discovered+associations%22}
}
@incollection{arules:Piatetsky-Shapiro:1991,
author = {G. Piatetsky-Shapiro},
title = {Discovery, Analysis, and Presentation of Strong Rules},
booktitle = {Knowledge Discovery in Databases},
publisher = {AAAI/MIT Press},
year = {1991},
editor = {G. Piatetsky-Shapiro and W.J. Frawley},
address = {Cambridge, MA},
abstract = {Introduces the measure LEVERAGE which is the simplest function which
satisfies his principles for rule-interest functions (0 if the variables
are statistically independent; monotonically increasing if the variables
occur more often together; monotonically decreasing if one of the
variables alone occurs more often).},
category = {kdd, measure},
google = {http://www.google.com/search?q=%22Discovery,+Analysis,+and+Presentation+of+Strong+Rules%22},
googlescholar = {http://scholar.google.com/scholar?q=%22Discovery,+Analysis,+and+Presentation+of+Strong+Rules%22}
}
@article{arules:Scheffer:2005,
author = {Tobias Scheffer},
title = {Finding association rules that trade support optimally against confidence},
journal = {Intelligent Data Analysis},
year = {2005},
volume = {9},
pages = {381--395},
number = {4},
abstract = {Introduces predictive accuracy which is the expected value of the
confidence of a rules with respect to the process underlying the
database. The author shows how predictive accuracy can be calculated
from confidence and support measured on a data set using a Bayesian
frequency correction (very simplified: confidence is discounted for
rules with low supports). Also an algorithm is presented which finds
the top n most predictive association rules (redundant rules with
a 0 predictive accuracy improvement are removed) and shows how to
estimate the prior distribution needed for the correction.},
category = {theory,measures},
publisher = {IOS Press},
google = {http://www.google.com/search?q=%22Finding+association+rules+that+trade+support+optimally+against+confidence%22},
googlescholar = {http://scholar.google.com/scholar?q=%22Finding+association+rules+that+trade+support+optimally+against+confidence%22}
}
@article{arules:Tan:2004,
author = {Pang-Ning Tan and Vipin Kumar and Jaideep Srivastava},
title = {Selecting the right objective measure for association analysis},
journal = {Information Systems},
year = {2004},
volume = {29},
pages = {293--313},
number = {4},
abstract = {Compare the properties of 21 objective measures (of interest). The
measures in general lack to agree with each other. However, the authors
show that if support-based pruning or table standardization (of the
contingency tables) is used, the measures become highly correlated.},
category = {measures},
publisher = {Elsevier Science Ltd.},
google = {http://www.google.com/search?q=%22Selecting+the+right+objective+measure+for+association+analysis%22},
googlescholar = {http://scholar.google.com/scholar?q=%22Selecting+the+right+objective+measure+for+association+analysis%22}
}
@article{arules:Geng:2006,
author = {Liqiang Geng and Howard J. Hamilton},
title = {Interestingness measures for data mining: A survey},
journal = {ACM Computing Surveys},
volume = {38},
number = {3},
year = {2006},
pages = {9},
publisher = {ACM},
address = {New York, NY, USA},
category = {measures},
google = {http://www.google.com/search?q=%22Interestingness+measures+for+data+mining:+A+survey%22},
googlescholar = {http://scholar.google.com/scholar?q=%22Interestingness+measures+for+data+mining:+A+survey%22}
}
This file was generated by bibtex2html 1.93.