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Title: Mining relationships among interval-based events for classification
Authors: Patel, D. 
Hsu, W. 
Lee, M.L. 
Keywords: Classifier for interval data
Interval-based event mining
Temporal relation
Issue Date: 2008
Citation: Patel, D.,Hsu, W.,Lee, M.L. (2008). Mining relationships among interval-based events for classification. Proceedings of the ACM SIGMOD International Conference on Management of Data : 393-404. ScholarBank@NUS Repository.
Abstract: Existing temporal pattern mining assumes that events do not have any duration. However, events in many real world applications have durations, and the relationships among these events are often complex. These relationships are modeled using a hierarchical representation that extends Allen's interval algebra. However, this representation is lossy as the exact relationships among the events cannot be fully recovered. In this paper, we augment the hierarchical representation with additional information to achieve a lossless representation. An efficient algorithm called IEMiner is designed to discover frequent temporal patterns from interval-based events. The algorithm employs two optimization techniques to reduce the search space and remove non-promising candidates. From the discovered temporal patterns, we build an interval-based classifier called IEClassifier to differentiate closely related classes. Experiments on both synthetic and real world datasets indicate the efficiency and scalability of the proposed approach, as well as the improved accuracy of IEClassifier. Copyright 2008 ACM.
Source Title: Proceedings of the ACM SIGMOD International Conference on Management of Data
ISBN: 9781605581026
ISSN: 07308078
DOI: 10.1145/1376616.1376658
Appears in Collections:Staff Publications

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