Advances in knowledge discovery and data mining
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
On Dual Mining: From Patterns to Circumstances, and Back
Proceedings of the 17th International Conference on Data Engineering
FARM: A Framework for Exploring Mining Spaces with Multiple Attributes
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
Discovery of Multiple-Level Association Rules from 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
Mining Associations by Pattern Structure in Large Relational Tables
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Discovering actionable patterns in event data
IBM Systems Journal
Discovering Periodic-Frequent Patterns in Transactional Databases
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
RMAIN: Association rules maintenance without reruns through data
Information Sciences: an International Journal
An efficiently algorithm based on itemsets-lattice and bitmap index for finding frequent itemsets
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Efficient mining regularly frequent patterns in transactional databases
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
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Frequent itemset mining aims at discovering patterns the supports of which are beyond a given threshold. In many applications, including network event management systems, which motivated this work, patterns are composed of items each described by a subset of attributes of a relational table. As it involves an exponential mining space, the efficient implementation of user preferences and mining constraints becomes the first priority for a mining algorithm. User preferences and mining constraints are often expressed using patterns’ attribute structures. Unlike traditional methods that mine all frequent patterns indiscriminately, we regard frequent itemset mining as a two-step process: the mining of the pattern structures and the mining of patterns within each pattern structure. In this paper, we present a novel architecture that uses pattern structures to organize the mining space. In comparison with the previous techniques, the advantage of our approach is two-fold: (i) by exploiting the interrelationships among pattern structures, execution times for mining can be reduced significantly; and (ii) more importantly, it enables us to incorporate high-level simple user preferences and mining constraints into the mining process efficiently. These advantages are demonstrated by our experiments using both synthetic and real-life datasets.