Efficiently mining long patterns from databases
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Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
FreeSpan: frequent pattern-projected sequential pattern mining
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SPADE: an efficient algorithm for mining frequent sequences
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MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
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PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
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VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
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Actions and Events in Interval Temporal Logic
Actions and Events in Interval Temporal Logic
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BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
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Mining relationships among interval-based events for classification
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Mining temporal interval relational rules from temporal data
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Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent arrangements of temporal intervals
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Relational Temporal Data Mining for Wireless Sensor Networks
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Margin-closed frequent sequential pattern mining
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An efficient algorithm for mining time interval-based patterns in large database
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
ARTEMIS: assessing the similarity of event-interval sequences
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Mining sequences of temporal intervals
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
IO3: interval-based out-of-order event processing in pervasive computing
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Distance measure for querying sequences of temporal intervals
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Mining recent temporal patterns for event detection in multivariate time series data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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A temporal pattern mining approach for classifying electronic health record data
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In this paper we study a new problem in temporal pattern mining: discovering frequent arrangements of temporal intervals. We assume that the database consists of sequences of events, where an event occurs during a time-interval. The goal is to mine arrangements of event intervals that appear frequently in the database. There are many applications where these type of patterns can be useful, including data network, scientific, and financial applications. Efficient methods to find frequent arrangements of temporal intervals using both breadth first and depth first search techniques are described. The performance of the proposed algorithms is evaluated and compared with other approaches on real datasets (American Sign Language streams and network data) and large synthetic datasets.