Sequential Pattern Mining Algorithms – Recent Trends

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Ambar Dutta
Sandeep Mukherjee

Abstract

Sequential pattern mining is a technique of data mining whose objective is to identify statistically relevant patterns within a database with time-related data. It has a wide range of applications in variety of domains like education, healthcare, bioinformatics, web usage mining, telecommunications, intrusion detection etc. At present, most of the real sequence databases are incremental in nature. So there is a need to explore incremental and distributed pattern mining algorithms. Periodic pattern mining is a technique to discover periodic pattern which may be a pattern that repeats itself after a specific time interval. It has a wide range of applications in weather prediction, stock market analysis, web usage recommendation etc. Moreover, uncertain frequent pattern mining has become a popular research domain among researchers, as many real-life databases at present consist of uncertain and incomplete data. In this paper, a novel attempt is made to incorporate a systematic literature review of state-of-the-art techniques of sequential pattern mining which ranges from incremental pattern mining, periodic pattern mining and uncertain frequent pattern mining. Researchers in the field of pattern mining will find it very useful to get the information about various algorithms of different types of pattern mining.

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Author Biography

Ambar Dutta, Amity Institute of Information Technology, Amity University Kolkata, West Bengal, India

Dr. Ambar Dutta did his Bachelors in Mathematics from Presidency College, Kolkata and Masters and Ph.D. from Jadavpur University, Kolkata. After serving in department of Computer Science and Engineering, Birla Institute of Technology, Mesra for 15 years, he is at present working as Associate Professor in Amity Institute of Information Technology, Amity University, Kolkata. Dr. Dutta authored a book and has published more than 50 papers in reputed journals/conferences. His research interest includes Image Processing, Data Analytics, Machine Learning, Information Retrieval. He is active reviewer of many reputed journals of Elsevier, Springer, IET. He is senior member of various professional bodies

References

Srikant R. and Agrawal R., “Mining sequential patterns: Generalizations and performance improvementsâ€, in Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology, pp 3–17, 1996.

Lin M. Y, Lee S. Y, “Incremental update on sequential patterns in large databasesâ€, in Proceedings of Tenth IEEE International Conference on Tools with Artificial Intelligence, Taipei, Taiwan, pp 24–31, 1998.

Parthasarathy S., Zaki M., Ogihara M., Dwarkadas S., “Incremental and Interactive Sequence Miningâ€, in Proceedings of the Eighth International Conference on Information and Knowledge Management, Kansas City, MO, USA, pp 251-258, 1999.

Masseglia, F., Poncelet, P., &Teisseire, M., “Incremental mining of sequential patterns in large databasesâ€, Data and Knowledge Engineering, Vol. 46, Iss. 1, pp 97-121, 2003.

Ozden B., Ramaswamy S., Silberschatz A., “Cyclic association rulesâ€, In Proceedings 14th International Conference on Data Engineering. Orlando, Florida, pp. 412-421, 1998

Han J., Gong W., Yin Y., “Efficient mining of partial periodic patterns in time series databasesâ€, in Proceedings 15th International Conference on Data Engineering, Sydney, NSW, Australia, pp. 106-115, 1999.

Elfeky M. G., Aref W. G., Elmagarmid A .K., “Incremental, online and merge mining of partial periodic patterns in time series databasesâ€, IEEE Transaction on Knowledge and Data Engineering, Vol. 16, No. 3, pp. 332-342, 2004.

Chen S.S., Huang T.C.K., Lin Z.M., “New and efficient knowledge discovery of partial periodic patterns with multiple minimum supports†, Journal of Systems and Software, Vol. 84, Iss 10, pp. 1638-1651, 2011.

Yang J., Wang W., and Yu P. S., (2003). “Mining Asynchronous Periodic Patterns in Time Series Dataâ€, IEEE Transaction on Knowledge and Data Engineering, Vol. 15, Iss 3, pp. 613-628, 2003.

Ma S., Hellerstein J., “Mining Partially Periodic Event Patterns with Unknown Periodsâ€, in Proceedings 17th International Conference on Data Engineering, Heidelberg, Germany, 2001.

Berberidis C., Aref W., Atallah M., Vlahavas I., Elmagarmid A., “Multiple and Partial Periodicity Mining in Time Series Databases†in Proceedings of the 15th European Conference on Artificial Intelligence, pp 370–374, 2002.

Indyk P., Koudas N., Muthukrishnan S., “Identifying representative trends in massive time series datasets using sketchesâ€, in Proceedings of the 26th International Conference on Very Large Databases, 2000.

Elfeky M. G., Aref W. G., Elmagarmid A. K., “STAGGER: Periodicity Mining of Data Streams using Expanding Sliding Windowsâ€, in Proceedings of the Sixth International Conference on Data Mining, Hong Kong, China, pp 188-199, 2006.

Elfeky M.G., Aref W.G., Elmagarmid A.K., “WARP: Time Warping for Periodicity Detectionâ€, in Proceedings of the Fifth IEEE International Conference on Data Mining, Houston, TX, USA, 2005.

Huang P., Liu C. J., Xiao Li, Chen J., “Wireless Spectrum Occupancy Prediction Based On Partial Periodic Pattern Miningâ€, in Proceedings of the IEEE 20th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, Washington, DC, USA, pp. 51-58, 2012.

Dutta M., Mahanta A. K., “Mining Calendar-Based Periodicities of Patterns in Temporal Dataâ€, in Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence, pp 243-248, 2009.

Dutta M., Mahanta A. K., “Detection of Calendar- Based Periodicities of Interval-Based Temporal Patternsâ€, International Journal of Data Mining & Knowledge Management Process, Vol.2, No.1, pp 17-31, 2012

Huang K. Y., Chang C.H., “SMCA: A General Model for Mining Asynchronous Periodic Patterns in Temporal Databasesâ€, IEEE Transactions on Knowledge and Data Engineering, Vol. 17, Iss. 6, pp 774–785, June 2005.

Maqbool F., Bashir S., and Baig A.R., “E-MAP: Efficiently Mining Asynchronous Periodic Patternsâ€, International Journal of Computer Science and Network Security, Vol. 6, No. 8A, Aug, 2006.

Yang J., Wang W., and Yu P. S., “Mining Asynchronous Periodic Patterns in Time Series Dataâ€, IEEE Transactions on Knowledge and Data Engineering, Vol. 15, Iss. 3, pp 613–628, March 2003.

Yeh J. S., Lin S. C., “A New Data Structure for Asynchronous Periodic Pattern Miningâ€, in Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication, New York, USA, pp 426-431, 2009.

Zhu Y. L., Li S. J., Bao N. N., Wan D. S., “Mining approximate periodic patterns in hydrological time seriesâ€, Journal of Computational Information Systems, Vol. 15, pp 6131-6144, 2013.

Amir A., Apostolico A., Eisenberg E., Landau G. M., Levy A., Lewenstein N., “Detecting approximate periodic patternsâ€, in Proceedings of the First Mediterranean conference on Design and Analysis of Algorithms, pp 1–12, 2012.

Zhang M., Kao B., Cheung D. W., Yip K. Y., “Mining Periodic Patterns with Gap Requirement from Sequencesâ€, Journal of ACM Transactions on Knowledge Discovery from Data, Vol. 1, Iss. 2, 2007.

Yang J., Wang W., Yu P., “InfoMiner+: Mining Partial Periodic Patterns with Gap Penalties†in Proceedings of the Second IEEE International Conference Data Mining, Maebashi City, Japan, 2002.

Ren, J., Lee, S.D., Chen, X., Kao, B., Cheng, R., Cheung, D., “Naive Bayes classification of uncertain dataâ€, in Proceedings of the Ninth IEEE International Conference on Data Mining, Miami, Florida, pp. 944-949, 2009.

Xu, L., & Hung, E., “Improving classification accuracy on uncertain data by considering multiple subclassesâ€. in Proceedings of the Twenty-Fifth Australasian Joint Conference on Artificial Intelligence, Sydney, Australia, pp 743-754, 2012.

Aggarwal, C. C., “Outlier Analysisâ€, Springer-Verlag New York, 2013.

Aggarwal, C. C., Yu, P. S., “Outlier detection with uncertain dataâ€, in Proceedings of the SIAM International Conference on Data Mining, SDM, pp 483–493, 2008.

Aggarwal, C. C. (ed.), “Managing and Mining Uncertain Dataâ€, Springer, Boston, MA, 2009.

Aggarwal, C. C., Yu, P. S., “A survey of uncertain data algorithms and applicationsâ€. IEEE Transactions on Knowledge and Data Engineering, Vol. 21, Iss. 5, pp 609–623, 2009.

Chui, C.-K., Kao, B., Hung, E., “Mining frequent itemsets from uncertain dataâ€. in Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining, pp 47–58, 2007.

Chui, C. K., Kao, B., “A decremental approach for mining frequent itemsets from uncertain dataâ€. in Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining, pp 64–75, 2008.

Leung, C. K. S., “Mining uncertain dataâ€, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery (WIDM), Vol. 1, No. 4, pp 316–329, 2013.

Aggarwal, C. C., Li, Y., Wang, J., Wang, J., “Frequent pattern mining with uncertain dataâ€, in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 29–38, 2009.

Leung, C. K. S., Tanbeer, S. K., “Fast tree-based mining of frequent itemsets from uncertain dataâ€. in Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I, pp 272–287, 2012.

Leung, C. K. S., Tanbeer, S. K., “PUF-tree: a compact tree structure for frequent pattern mining of uncertain dataâ€, in: Pei J., Tseng V.S., Cao L., Motoda H., Xu G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science, vol 7818, Springer, Berlin, Heidelberg, pp 13-25, 2013.

Calders, T., Garboni, C., Goethals, B., “Efficient pattern mining of uncertain data with samplingâ€. in: Zaki M.J., Yu J.X., Ravindran B., Pudi V. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2010. Lecture Notes in Computer Science, vol 6118, Springer, Berlin, Heidelberg, pp 480-487, 2010.

Budhia, B. P., Cuzzocrea, A., Leung, C. K. S., “Vertical frequent pattern mining from uncertain dataâ€. Frontiers in Artificial Intelligence and Applications, IOS Press, Volume 243, pp 1273-1282, 2012.

Leung, C. K. S., Tanbeer, S. K., Budhia, B. P., Zacharias, L. C., “Mining probabilistic datasets verticallyâ€, in Proceedings of the 16th International Database Engineering & Applications Symposium, pp 199–204, 2012.