Fast subsequence matching in time-series databases
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Segment-based approach for subsequence searches in sequence databases
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PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
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Fast time series classification using numerosity reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Enhanced 1-NN time series classification using badness of records
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ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
An approach to dimensionality reduction in time series
Information Sciences: an International Journal
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Sequence-data mining plays a key role in many scientific studies and real-world applications such as bioinformatics, data stream, and sensor networks, where sequence data are processed and their semantics interpreted. In this paper we address two relevant issues: sequence-data representation, and representation-to-semantics mapping. For representation, since the best one is dependent upon the application being used and even the type of query, we propose representing sequence data in multiple views. For each representation, we propose methods to construct a valid kernel as the distance function to measure similarity between sequences. For mapping, we then find the best combination of the individual distance functions, which measure similarity of different views, to depict the target semantics. We propose a super-kernel function-fusion scheme to achieve the optimal mapping. Through theoretical analysis and empirical studies on UCI and real world datasets, we show our approach of multi-view representation and fusion to be mathematically valid and very effective for practical purposes.