Practitioner problems in need of database research
ACM SIGMOD Record
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Pincer Search: A New Algorithm for Discovering the Maximum Frequent Set
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Online Generation of Association Rules
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Set-Oriented Mining for Association Rules in Relational Databases
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Knowledge Discovery in Databases: An Attribute-Oriented Approach
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Mining dependence rules by finding largest itemset support quota
Proceedings of the 2004 ACM symposium on Applied computing
Experiences in building a tool for navigating association rule result sets
ACSW Frontiers '04 Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
Essential classification rule sets
ACM Transactions on Database Systems (TODS)
Associative text categorization exploiting negated words
Proceedings of the 2006 ACM symposium on Applied computing
A novel distance-based classifier built on pattern ranking
Proceedings of the 2009 ACM symposium on Applied Computing
Mining non-coincidental rules without a user defined support threshold
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Using background knowledge to rank itemsets
Data Mining and Knowledge Discovery
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
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A new model to evaluate dependencies in data mining problems is presented and discussed. The well-known concept of the association rule is replaced by the new definition of dependence value, which is a single real number uniquely associated with a given itemset. Knowledge of dependence values is sufficient to describe all the dependencies characterizing a given data mining problem. The dependence value of an itemset is the difference between the occurrence probability of the itemset and a corresponding “maximum independence estimate.” This can be determined as a function of joint probabilities of the subsets of the itemset being considered by maximizing a suitable entropy function. So it is possible to separate in an itemset of cardinaltiy k the dependence inherited from its subsets of cardinality (k − 1) and the specific inherent dependence of that itemset. The absolute value of the difference between the probability p(i) of the event i that indicates the prescence of the itemset {a,b,... } and its maximum independence estimate is constant for any combination of values of Q &angl0; a,b,... &angr0; Q. In1paddition, the Boolean function specifying the combination of values for which the dependence is positive is a parity function. So the determination of such combinations is immediate. The model appears to be simple and powerful.