An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 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
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Information Systems (TOIS)
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
A template model for multidimensional inter-transactional association rules
The VLDB Journal — The International Journal on Very Large Data Bases
A fuzzy data mining algorithm for incremental mining of quantitative sequential patterns
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
An efficient algorithm for mining frequent inter-transaction patterns
Information Sciences: an International Journal
A new algorithm for data discretization and feature selection
Proceedings of the 2008 ACM symposium on Applied computing
On mining multi-time-interval sequential patterns
Data & Knowledge Engineering
Proceedings of the 2010 ACM Symposium on Applied Computing
Hi-index | 0.00 |
Technological advancement has enabled improvements in the technology of sensors and satellites used to gather climate data. The time series mining is an important tool to analyze the huge quantity of climate data. Here, we propose the Sequential Association Rules from Time series - SART method to mine association rules in time series that keeps the information of time between related events through an overlapped sliding-window approach. Also the proposed method mines association rules, while the previous ones produce frequent sequences, adding the semantic information of confidence, which was not previously defined by sequential patterns. Experiments were conducted with real data collected from climate sensors. The results showed that the proposed method increases the number of mined patterns when compared with the traditional sequential mining, revealing related events that occur over time. Also, the method adds the semantic information related to the confidence and time to the mined patterns.