Proceedings of the 18th international conference on World wide web
Learn to compress and restore sequential data
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
VOGUE: A variable order hidden Markov model with duration based on frequent sequence mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Finding semantics in time series
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Probabilistic user modeling in the presence of drifting concepts
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Identifying event-related bursts via social media activities
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
A vlHMM approach to context-aware search
ACM Transactions on the Web (TWEB)
Personalized news recommendation with context trees
Proceedings of the 7th ACM conference on Recommender systems
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In this paper, we propose a novel and general approach for time-series data mining. As an alternative to traditional ways of designing specific algorithm to mine certain kind of pattern directly from the data, our approach extracts the temporal structure of the time-series data by learning Markovian models, and then uses well established methods to efficiently mine a wide variety of patterns from the topology graph of the learned models. We consolidate the approach by explaining the use of some well-known Markovian models on mining several kinds of patterns. We then present a novel high-order hidden Markov model, the variable-length hidden Markov model (VLHMM), which combines the advantages of well-known Markovian models and has the superiority in both efficiency and accuracy. Therefore, it can mine a much wider variety of patterns than each of prior Markovian models. We demonstrate the power of VLHMM by mining four kinds of interesting patterns from 3D motion capture data, which is typical for the high-dimensionality and complex dynamics.