Understanding planner behavior
Artificial Intelligence - Special volume on planning and scheduling
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Temporal sequence learning and data reduction for anomaly detection
ACM Transactions on Information and System Security (TISSEC)
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
An Algorithm for Segmenting Categorical Time Series into Meaningful Episodes
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
From interaction data to plan libraries: a clustering approach
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Removing biases in unsupervised learning of sequential patterns
Intelligent Data Analysis
Sequence classification using statistical pattern recognition
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
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Unsupervised sequence learning is important to many applications. A learner is presented with unlabeled sequential data, and must discover sequential patterns that characterize the data. Popular approaches to such learning include statistical analysis and frequency based methods. We empirically compare these approaches and find that both approaches suffer from biases toward shorter sequences, and from inability to group together multiple instances of the same pattern. We provide methods to address these deficiencies, and evaluate them extensively on several synthetic and real-world data sets. The results show significant improvements in all learning methods used.