Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
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
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Linear pattern matching algorithms
SWAT '73 Proceedings of the 14th Annual Symposium on Switching and Automata Theory (swat 1973)
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
Visually mining and monitoring massive time series
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Visualizing and discovering non-trivial patterns in large time series databases
Information Visualization
Making clustering in delay-vector space meaningful
Knowledge and Information Systems
Motif discovery in physiological datasets: A methodology for inferring predictive elements
ACM Transactions on Knowledge Discovery from Data (TKDD)
A mining technique using n-grams and motion transcripts for body sensor network data repository
WH '10 Wireless Health 2010
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Previous studies on frequent pattern discovery from temporal sequence mainly consider finding global patterns, where every record in a sequence contributes to support the patterns. In this paper, we present a novel problem class that is the discovery of local sequential patterns, which only a subsequence of the original sequence exhibits. The problem has a two-dimensional solution space consisting of patterns and temporal features, therefore it is impractical that use traditional methods on this problem directly in terms of either time complexity or result validity. Our approach is to maintain a suffix-tree-like index to support efficiently locating and counting of local patterns. Based on the index, a method is proposed for discovering such patterns. We have analyzed the behavior of the problem and evaluated the performance of our algorithm on both synthetic and real data. The results correspond with the definition of our problem and verify the superiority of our method.