The nature of statistical learning theory
The nature of statistical learning theory
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Pattern Recognition and Neural Networks
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An iterative hypothesis-testing strategy for pattern discovery
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MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
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IEEE Transactions on Knowledge and Data Engineering
A study of the effects of bias in criterion functions for temporal data clustering
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Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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Constructing comprehensive summaries of large event sequences
ACM Transactions on Knowledge Discovery from Data (TKDD)
Rule generation for categorical time series with Markov assumptions
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Alternative Approach to Tree-Structured Web Log Representation and Mining
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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In this paper we investigate the general problem of discovering recurrent patterns that are embedded in categorical sequences. An important real-world problem of this nature is motif discovery in DNA sequences. We investigate the fundamental aspects of this data mining problem that can make discovery "easy" or "hard." We present a general framework for characterizing learning in this context by deriving the Bayes error rate for this problem under a Markov assumption. The Bayes error framework demonstrates why certain patterns are much harder to discover than others. It also explains the role of different parameters such as pattern length and pattern frequency in sequential discovery. We demonstrate how the Bayes error can be used to calibrate existing discovery algorithms, providing a lower bound on achievable performance. We discuss a number of fundamental issues that characterize sequential pattern discovery in this context, present a variety of empirical results to complement and verify the theoretical analysis, and apply our methodology to real-world motif-discovery problems in computational biology.