SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Constraint-based mining of episode rules and optimal window sizes
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Sequential pattern mining with time intervals
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Improving Time Series Forecasting by Discovering Frequent Episodes in Sequences
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Clustering preprocessing to improve time series forecasting
AI Communications
Recursive sequence mining to discover named entity relations
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Sequential patterns to discover and characterise biological relations
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
What about sequential data mining techniques to identify linguistic patterns for stylistics?
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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Among the family of the local patterns, episodes are commonly used when mining a single or multiple sequences of discrete events. An episode reflects a qualitative relation is-followed-by over event types, and the refinement of episodes to incorporate quantitative temporal information is still an on going research, with many application opportunities. In this paper, focusing on serial episodes, we design such a refinement called quantitative episodes and give a corresponding extraction algorithm. The three most salient features of these quantitative episodes are: (1) their ability to characterize main groups of homogeneous behaviors among the occurrences, according to the duration of the is-followed-by steps, and providing quantitative bounds of these durations organized in a tree structure; (2) the possibility to extract them in a complete way; and (3) to perform such extractions at the cost of a limited overhead with respect to the extraction of standard episodes.