Turbo-charging vertical mining of large databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Top.K Frequent Closed Patterns without Minimum Support
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Collaborative Filtering by Mining Association Rules from User Access Sequences
WIRI '05 Proceedings of the International Workshop on Challenges in Web Information Retrieval and Integration
Enhancing Mobile Web Access Using Intelligent Recommendations
IEEE Intelligent Systems
Discovering Periodic-Frequent Patterns in Transactional Databases
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Mining changes in customer behavior in retail marketing
Expert Systems with Applications: An International Journal
In-depth behavior understanding and use: The behavior informatics approach
Information Sciences: an International Journal
Mining Regular Patterns in Incremental Transactional Databases
APWEB '10 Proceedings of the 2010 12th International Asia-Pacific Web Conference
Towards efficient mining of periodic-frequent patterns in transactional databases
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Mining regular patterns in data streams
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
| Hi-index | 0.00 |
Association rule discovery based on support-confidence framework 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.