Principles and practice of information theory
Principles and practice of information theory
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Prediction with local patterns using cross-entropy
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Small is beautiful: discovering the minimal set of unexpected patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining asynchronous periodic patterns in time series data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Mining Partially Periodic Event Patterns with Unknown Periods
Proceedings of the 17th International Conference on Data Engineering
Mining Frequent Itemsets Using Support Constraints
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Efficient Mining of Statistical Dependencies
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Mining long sequential patterns in a noisy environment
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Mining Asynchronous Periodic Patterns in Time Series Data
IEEE Transactions on Knowledge and Data Engineering
Introducing Uncertainty into Pattern Discovery in Temporal Event Sequences
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Journal of Computer Science and Technology
ADMiRe: An Algebraic Data Mining Approach to System Performance Analysis
IEEE Transactions on Knowledge and Data Engineering
Discovery of Periodic Patterns in Spatiotemporal Sequences
IEEE Transactions on Knowledge and Data Engineering
Unsupervised pattern mining from symbolic temporal data
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
Mining partial periodic correlations in time series
Knowledge and Information Systems
Mining fuzzy periodic association rules
Data & Knowledge Engineering
Improving Anomaly Detection Event Analysis Using the EventRank Algorithm
AIMS '07 Proceedings of the 1st international conference on Autonomous Infrastructure, Management and Security: Inter-Domain Management
Finding anomalous periodic time series
Machine Learning
A new data structure for asynchronous periodic pattern mining
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
Mining periodic patterns in spatio-temporal sequences at different time granularities
Intelligent Data Analysis
Efficient discovery of unusual patterns in time series
New Generation Computing
Mining Musical Patterns: Identification of Transposed Motives
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Finding event-oriented patterns in long temporal sequences
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Mining periodic behaviors for moving objects
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining transposed motifs in music
Journal of Intelligent Information Systems
New and efficient knowledge discovery of partial periodic patterns with multiple minimum supports
Journal of Systems and Software
Mining approximate motifs in time series
DS'06 Proceedings of the 9th international conference on Discovery Science
TrajPattern: mining sequential patterns from imprecise trajectories of mobile objects
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Periodic pattern analysis of non-uniformly sampled stock market data
Intelligent Data Analysis
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In this paper, we focus on mining surprising periodic patterns in a sequence of events. In many applications, e.g., computational biology, an infrequent pattern is still considered very significant if its actual occurrence frequency exceeds the prior expectation by a large margin. The traditional metric, such as support, is not necessarily the ideal model to measure this kind of surprising patterns because it treats all patterns equally in the sense that every occurrence carries the same weight towards the assessment of the significance of a pattern regardless of the probability of occurrence. A more suitable measurement, information, is introduced to naturally value the degree of surprise of each occurrence of a pattern as a continuous and monotonically decreasing function of its probability of occurrence. This would allow patterns with vastly different occurrence probabilities to be handled seamlessly. As the accumulated degree of surprise of all repetitions of a pattern, the concept of information gain is proposed to measure the overall degree of surprise of the pattern within a data sequence. The bounded information gain property is identified to tackle the predicament caused by the violation of the downward closure property by the information gain measure and in turn provides an efficient solution to this problem. Empirical tests demonstrate the efficiency and the usefulness of the proposed model.