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PAKDD'11: Workshop on Behavior Informatics, Shenzhen : Chine (2001)
Efficient mining Top-k regular-frequent itemset using compressed tidsets
Komate Amphawan 1, 2, 3, Philippe Lenca 1, 2, Athasit Surarerks 3

Association rule discovery based on support-confidence frame-work is an important task in data mining. However, the occurrence frequency (support) of a pattern (itemset) may not be a sufficient criterion for discovering interesting patterns. Temporal regularity, which can be a trace of behavior, with frequency behavior can be revealed as an important key in several applications. A pattern can be regarded as a regular pattern if it occurs regularly in a user-given period. In this paper, we consider the problem of mining top-k regular-frequent itemsets from transactional databases without support threshold. A new concise representation, called compressed transaction-ids set (compressed tidset), and a single pass algorithm, called TR-CT (Top-k Regular frequent itemset mining based on Compressed Tidsets), are proposed to maintain occurrence information of patterns and discover k regular itemsets with highest supports, respectively. Experimental results show that the use of the compressed tidset representation achieves highly efficiency in terms of execution time and memory consumption, especially on dense datasets.
1 :  Département Logique des Usages, Sciences sociales et Sciences de l'Information (LUSSI)
Institut Mines-Télécom – Télécom Bretagne – PRES Université Européenne de Bretagne (UEB)
2 :  Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC)
CNRS : UMR3192 – Université de Bretagne Occidentale (UBO) – Université de Bretagne Sud (UBS) – Institut Mines-Télécom – Télécom Bretagne – PRES Université Européenne de Bretagne (UEB) – Institut Supérieur des Sciences et Technologies de Brest (ISSTB)
3 :  Engineering Laboratory in Theoretical Enumerable System (ELITE)
University of Chulalongkorn
Informatique/Algorithme et structure de données


Informatique/Base de données

Informatique/Intelligence artificielle
Frequent itemsets – Regular itemsets – Top-k itemsets
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amphawan_etal_BI-PAKDD_2011.pdf(455.8 KB)