Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Efficiently mining long patterns from databases
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
An approach to discovering temporal association rules
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
ACM Transactions on Information Systems (TOIS)
Towards long pattern generation in dense databases
ACM SIGKDD Explorations Newsletter
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Book review: Three perspectives of data mining
Artificial Intelligence
Discovering calendar-based temporal association rules
Data & Knowledge Engineering - Special issue: Temporal representation and reasoning
Efficient Mining of Intertransaction Association Rules
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
On Mining General Temporal Association Rules in a Publication Database
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
Local and Global Methods in Data Mining: Basic Techniques and Open Problems
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Discovering Temporal Relation Rules Mining from Interval Data
EurAsia-ICT '02 Proceedings of the First EurAsian Conference on Information and Communication Technology
A template model for multidimensional inter-transactional association rules
The VLDB Journal — The International Journal on Very Large Data Bases
Tree Structures for Mining Association Rules
Data Mining and Knowledge Discovery
Temporal representation and reasoning in artificial intelligence: A review
Mathematical and Computer Modelling: An International Journal
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The incorporation of temporal semantic into the traditional data mining techniques has caused the creation of a new area called Temporal Data Mining. This incorporation is especially necessary if we want to extract useful knowledge from dynamic domains, which are time-varying in nature. However, this process is computationally complex, and therefore it poses more challenges on efficient processing that non-temporal techniques. Based in the inter-transactional framework, in [11] we proposed an algorithm named TSET for mining temporal patterns (sequences) from datasets which uses a unique tree-based structure for storing all frequent patterns discovered in the mining process. However, in each data mining process, the algorithm must generate the whole structure from scratch. In this work, we propose an extension which consists in the reusing of structures generated in previous data mining process in order to reduce the execution time of the algorithm.