Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Combinatorial pattern discovery for scientific data: some preliminary results
SIGMOD '94 Proceedings of the 1994 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
Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Mining optimized association rules for numeric attributes
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Scalable parallel data mining for association rules
SIGMOD '97 Proceedings of the 1997 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
Designing Templates for Mining Association Rules
Journal of Intelligent Information Systems
Query flocks: a generalization of association-rule mining
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Breaking the barrier of transactions: mining inter-transaction association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining inter-transaction associations with templates
Proceedings of the eighth international conference on Information and knowledge management
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
ACM Transactions on Information Systems (TOIS)
Time Series Analysis
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Scalable Techniques for Mining Causal Structures
Data Mining and Knowledge Discovery
Efficient Mining of Association Rules in Distributed Databases
IEEE Transactions on Knowledge and Data Engineering
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Efficient Retrieval of Similar Time Sequences Under Time Warping
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Mining Optimized Association Rules with Categorical and Numeric Attributes
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
HierarchyScan: A Hierarchical Similarity Search Algorithm for Databases of Long Sequences
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Scalable Techniques for Mining Causal Structures
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Querying Continuous Time Sequences
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
On the Discovery of Interesting Patterns in Association Rules
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Mining Surprising Patterns Using Temporal Description Length
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Computing Iceberg Queries Efficiently
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Mining Frequent Itemsets Using Support Constraints
VLDB '00 Proceedings of the 26th 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
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series 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
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
A template model for multidimensional inter-transactional association rules
The VLDB Journal — The International Journal on Very Large Data Bases
Data mining for path traversal patterns in a web environment
ICDCS '96 Proceedings of the 16th International Conference on Distributed Computing Systems (ICDCS '96)
Finding Interesting Associations without Support Pruning
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Online Data Mining for Co-Evolving Time Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
An efficient cluster and decomposition algorithm for mining association rules
Information Sciences—Informatics and Computer Science: An International Journal
An efficient algorithm for mining frequent inter-transaction patterns
Information Sciences: an International Journal
On discovery of soft associations with "most" fuzzy quantifier for item promotion applications
Information Sciences: an International Journal
An efficient algorithm for mining closed inter-transaction itemsets
Data & Knowledge Engineering
Efficient single-pass frequent pattern mining using a prefix-tree
Information Sciences: an International Journal
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The problem of mining multidimensional inter-transactional association rules was recently introduced in [ACM Trans. Inform. Syst. 18(4) (2000) 423; Proc. of the ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Seattle, Washington, June 1998, p. 12:1]. It extends the scope of mining association rules from traditional single-dimensional intra-transactional associations to multidimensional inter-transactional associations. Inter-transactional association rules can represent not only the associations of items happening within transactions as traditional intra-transactional association rules do, but also the associations of items among different transactions under a multidimensional context. "After McDonald and Burger King open branches, KFC will open a branch two months later and one mile away" is an example of such rules. In this paper, we extend the previous problem definition based on context expansion, and present a more general form of association rules, named generalized multidimensional inter-transactional association rules. An algorithm for mining such generalized inter-transactional association rules is presented by extension of a priori. We report our experiments on applying the algorithm to both real-life and synthetic data sets. Empirical evaluation shows that with the generalized inter-transactional association rules, more comprehensive and interesting association relationships can be detected from data sets.