e-approximations with minimum packing constraint violation (extended abstract)
STOC '92 Proceedings of the twenty-fourth annual ACM symposium on Theory of computing
A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
On the approximation of curves by line segments using dynamic programming
Communications of the ACM
DNA segmentation as a model selection process
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Principles of data mining
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Finding recurrent sources in sequences
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Time Series Segmentation for Context Recognition in Mobile Devices
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Improved Combinatorial Algorithms for the Facility Location and k-Median Problems
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Efficient inference on sequence segmentation models
ICML '06 Proceedings of the 23rd international conference on Machine learning
Optimal Segmentation Using Tree Models
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Discovering temporal hidden contexts in web sessions for user trail prediction
Proceedings of the 22nd international conference on World Wide Web companion
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Many sequential data sets have a segmental structure, and similar types of segments occur repeatedly. We consider sequences where the underlying phenomenon of interest is governed by a small set of models that change over time. Potential examples of such data are environmental, genomic, and economic sequences. Given a target sequence and a (possibly multivariate) sequence of observation values, we consider the problem of finding a small collection of models that can be used to explain the target phenomenon in a piecewise fashion using the observation values. We assume the same model will be used for multiple segments. We give an algorithm for this task based on first segmenting the sequence using dynamic programming, and then using k-median or facility location techniques to find the optimal set of models. We report on some experimental results.