Discovering patterns in sequences of events
Artificial Intelligence
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
On the Discovery of Interesting Patterns in Association Rules
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Discovering Temporal Patterns in Multiple Granularities
TSDM '00 Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers
Discovering Temporal Patterns for Interval-Based Events
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
An Integrated Query and Mining System for Temporal Association Rules
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
A bibliography of temporal, spatial and spatio-temporal data mining research
ACM SIGKDD Explorations Newsletter
Clustering of streaming time series is meaningless
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A framework for representing navigational patterns as full temporal objects
ACM SIGecom Exchanges
A review on time series data mining
Engineering Applications of Artificial Intelligence
Accurate symbolization of time series
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Substructure clustering: a novel mining paradigm for arbitrary data types
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Short communication: Selective Subsequence Time Series clustering
Knowledge-Based Systems
| Hi-index | 0.00 |
In recent years, there bas been increased interest in using data mining techniques to extract temporal rules from temporal sequences. Local temporal rules, which only a subsequence exhibits, are actually very common in practice. Efficient discovery of the time duration in which temporal rules are valid could benefit KDD of many real applications. In this paper, we present a novel problem class that is the discovery of the distribution of temporal rules. We simplify the mining problem and depict a model that could represent this knowledge clearly, uniquely and efficiently. Our methods include four online dividing strategies for different mining interest, an incremental algorithm for measuring rule-sets, and an algorithm for mining this knowledge. We have analyzed the behavior of the problem and our algorithms with both synthetic data and real data. The results correspond with the definition of our problem and reveal a kind of novel knowledge.