C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Maintaining knowledge about temporal intervals
Communications of the ACM
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Mining hepatitis data with temporal abstraction
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Frequent Arrangements of Temporal Intervals
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Mining Nonambiguous Temporal Patterns for Interval-Based Events
IEEE Transactions on Knowledge and Data Engineering
CASEE: a hierarchical event representation for the analysis of videos
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Generalization of pattern-growth methods for sequential pattern mining with gap constraints
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Temporal mining for interactive workflow data analysis
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Interval event stream processing
Proceedings of the Third ACM International Conference on Distributed Event-Based Systems
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
Interval-orientation patterns in spatio-temporal databases
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
Probabilistic temporal multimedia data mining
ACM Transactions on Intelligent Systems and Technology (TIST)
Complex event pattern detection over streams with interval-based temporal semantics
Proceedings of the 5th ACM international conference on Distributed event-based system
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
IO3: interval-based out-of-order event processing in pervasive computing
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Recognizing players' activities and hidden state
Proceedings of the 6th International Conference on Foundations of Digital Games
Activity recognition with finite state machines
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Finding representative objects using link analysis ranking
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Database research at the National University of Singapore
ACM SIGMOD Record
Frequent episode mining within the latest time windows over event streams
Applied Intelligence
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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.