Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Using association rules for product assortment decisions: a case study
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
An approach to discovering temporal association rules
SAC '00 Proceedings of the 2000 ACM symposium on Applied computing - Volume 1
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Discovering calendar-based temporal association rules
Data & Knowledge Engineering - Special issue: Temporal representation and reasoning
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and 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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Discovering Temporal Relation Rules Mining from Interval Data
EurAsia-ICT '02 Proceedings of the First EurAsian Conference on Information and Communication Technology
An effective mining approach for up-to-date patterns
Expert Systems with Applications: An International Journal
Temporal approach to association rule mining using t-tree and p-tree
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
| Hi-index | 12.05 |
Mining interesting and useful frequent patterns from large databases attracts much attention in recent years. Among the mining approaches, finding temporal patterns and regularities is very important due to its practicality. In the past, Hong et al. proposed the up-to-date patterns, which were frequent within their up-to-date lifetime. Formally, an up-to-date pattern is a pair with the itemset and its valid corresponding lifetime in which the user-defined minimum support threshold must be satisfied. They also proposed an Apriori-like approach to find the up-to-date patterns. This paper thus proposes the up-to-date pattern tree (UDP tree) to keep the up-to-date 1-patterns in a tree structure for reducing database scan. It is similar to the FP-tree structure but more complex due to the requirement of up-to-date patterns. The UDP-growth mining approach is also designed to find the up-to-date patterns from the UDP tree. The experimental results show that the proposed approach has a better performance than the level-wise mining algorithm.