Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining relational patterns from multiple relational tables
Decision Support Systems - From information retrieval to knowledge management: enabling technologies and best practices
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
TBAR: An efficient method for association rule mining in relational databases
Data & Knowledge Engineering
Data Mining with optimized two-dimensional association rules
ACM Transactions on Database Systems (TODS)
Discovering associations with numeric variables
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
An evolutionary algorithm to discover numeric association rules
Proceedings of the 2002 ACM symposium on Applied computing
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Mining Optimized Association Rules with Categorical and Numeric Attributes
IEEE Transactions on Knowledge and Data Engineering
A Statistical Theory for Quantitative Association Rules
Journal of Intelligent Information Systems
Mining Optimized Gain Rules for Numeric Attributes
IEEE Transactions on Knowledge and Data Engineering
Mining Optimized Association Rules with Categorical and Numeric Attributes
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Mining the Smallest Association Rule Set for Predictions
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Discovery of Association Rules in Tabular Data
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Discovering Numeric Association Rules via Evolutionary Algorithm
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
On the discovery of significant statistical quantitative rules
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithms for mining association rules in bag databases
Information Sciences—Informatics and Computer Science: An International Journal
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
A trie-based APRIORI implementation for mining frequent item sequences
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Mining quantitative correlated patterns using an information-theoretic approach
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
An information-theoretic approach to quantitative association rule mining
Knowledge and Information Systems
Bottom-up discovery of frequent rooted unordered subtrees
Information Sciences: an International Journal
Efficient single-pass frequent pattern mining using a prefix-tree
Information Sciences: an International Journal
Top-down mining of frequent closed patterns from very high dimensional data
Information Sciences: an International Journal
FIUT: A new method for mining frequent itemsets
Information Sciences: an International Journal
QuantMiner: a genetic algorithm for mining quantitative association rules
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Integrated Computer-Aided Engineering
Toward boosting distributed association rule mining by data de-clustering
Information Sciences: an International Journal
Cosine interesting pattern discovery
Information Sciences: an International Journal
Generalized association rule mining with constraints
Information Sciences: an International Journal
High utility pattern mining using the maximal itemset property and lexicographic tree structures
Information Sciences: an International Journal
Information Sciences: an International Journal
Mining frequent patterns and association rules using similarities
Expert Systems with Applications: An International Journal
Optimal leverage association rules with numerical interval conditions
Intelligent Data Analysis
Hi-index | 0.07 |
Most methods for mining association rules from tabular data mine simple rules which only use the equality operator ''='' in their items. For quantitative attributes, approaches tend to discretize domain values by partitioning them into intervals. Limiting the operator only to ''='' results in many interesting frequent patterns that may not be identified. It is obvious that where there is an order between objects, operators such as greater than or less than a given value are as important as the equality operator. This motivates us to extend association rules, from the simple equality operator, to a more general set of operators. We address the problem of mining general association rules in tabular data where rules can have all operators {=,,=} in their antecedent part. The proposed algorithm, mining general rules (MGR), is applicable to datasets with discrete-ordered attributes and on quantitative discretized attributes. The proposed algorithm stores candidate general itemsets in a tree structure in such a way that supports of complex itemsets can be recursively computed from supports of simpler itemsets. The algorithm is shown to have benefits in terms of time complexity, memory management and has good potential for parallelization.