Mining Minimal Distinguishing Subsequence Patterns with Gap Constraints
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Mining minimal distinguishing subsequence patterns with gap constraints
Knowledge and Information Systems
A fast algorithm for finding frequent episodes in event streams
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised pattern mining from symbolic temporal data
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
Mining Interventions from Parallel Event Sequences
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
Softening the blow of frequent sequence analysis: soft constraints and temporal accuracy
International Journal of Web Engineering and Technology
A frequent pattern based framework for event detection in sensor network stream data
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
Mining complex patterns across sequences with gap requirements
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Extracting trees of quantitative serial episodes
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
An algorithmic approach to event summarization
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Episode rule-based prognosis applied to complex vacuum pumping systems using vibratory data
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Data-driven prognosis applied to complex vacuum pumping systems
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Mining closed episodes with simultaneous events
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining association rules in long sequences
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Mining closed episodes from event sequences efficiently
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Algorithms to discover complete frequent episodes in sequences
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Data Mining and Knowledge Discovery
Discovering lag intervals for temporal dependencies
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining complex event patterns in computer networks
NFMCP'12 Proceedings of the First international conference on New Frontiers in Mining Complex Patterns
Mining serial-episode rules using minimal occurrences with gap constraint
ICCSA'13 Proceedings of the 13th international conference on Computational Science and Its Applications - Volume 1
Editorial: Pattern-growth based frequent serial episode discovery
Data & Knowledge Engineering
Data mining of serial-episode association rules using gap-constrained minimal occurrences
International Journal of Business Intelligence and Data Mining
Discovering episodes with compact minimal windows
Data Mining and Knowledge Discovery
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Episode rules are patterns that can be extracted from a large event sequence, to suggest to experts possible dependencies among occurrences of event types. The corresponding mining approaches have been designed to find rules under a temporal constraint that specifies the maximum elapsed time between the first and the last event of the occurrences of the patterns (i.e., a window size constraint). In some applications the appropriate window size is not known, and furthermore, this size is not the same for different rules. To cope with this class of applications, it has been recently proposed in [2] to specifying the maximal elapsed time between two events (i.e., a maximum gap constraint) instead of a window size constraint. Unfortunately, we show that the algorithm proposed to handle the maximum gap constraint is not complete. In this paper we present a sound and complete algorithm to mine episode rules under the maximum gap constraint, and propose to find, for each rule, the window size corresponding to a local maximum of confidence. We show that the extraction can be efficiently performed in practice on real and synthetic datasets. Finally the experiments show that the notion of local maximum of confidence is significant in practice, since no local maximum are found in random datasets, while they can be found in real ones.