A Frequent Pattern Based Time Series Classification Framework

Boukerche. Handbook of Algorithms for Qireless Networking and　Mobile Computing. Chapman Hall/CRC, 2005.[2]Aach J and Church G. Aligning gene expression time series with time warping algorithms. Bioinformatics, 2001, 17(6), 495-508.[3]Laxman S. Stream prediction using a generative model based on frequent episodes in event sequences. Proceeding of Knowledge Discovery and Data Mining Conference 2008, Las Vegas, Nevada, USA,30 Jul. 2008: 453-461.[4]Vladimir Vapnik. The Nature of Statistical Learning Theory. New York: Springer Verlag, 1999, Chapter 4.[5]Lin J, Keogh E, Lonardi S, and Chiu B. A symbolic representation of time series with implications for streaming algorithms. Proceedings of the 8th ACM SIGMOD workshop on research issues in data mining and knowledge discovery, San Diego, California, 9 Jun. 2003: 2-11.[6]Cheng H, Yan X, Han J, and Hsu C W. Discriminative frequent pattern analysis for effective classification. Proceeding of International Conference on Data Engineering 2007, Istanbul, 17 April, 2007: 716-725.[7]Liu B, Hsu W, and Ma Y. Integrating classification and association rule mining. Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing, London, UK, Springer- Verlag?1999: 443-447.[8]Patel D, Hsu W, and Lee M L. Mining relationships among interval-based events for classification, Proceeding of International Conference on Management of Data / Principles of Database Systems, Vancouver, Canada, 10 Jun. 2008: 393-404.[9]Laxman S, Sastry P S, and Unnikrishnan K P. Discovering frequent episodes and learning Hidden Markov Models: A formal connection[J].IEEE Transactions on Knowledge and Data Engineering.2005, 17(11):1505-1517[10]Yang Y and Pedersen J O. A comparative study on feature selection in text categorization. Proceeding of International Conference on Machine Learning, San Francisco, USA, 8 Jul. 1997: 412-420.