Abstract
In knowledge discovery, data mining of time series data has many important applications. Especially, sequential patterns and periodic patterns, which evolved from the association rule, have been applied in many useful practices. This paper presents another useful concept, the periodic clustering sequential (PCS) pattern, which uses clustering to mine valuable information from temporal or serially ordered data in a period of time. For example, one can cluster patients according to symptoms of the illness under study, but this may just result in several clusters with specific symptoms for analyzing the distribution of patients. Adding time series analysis to the above investigation, we can examine the distribution of patients over the same or different seasons. For policymakers, the PCS pattern is more useful than traditional clustering result and provides a more effective support of decision-making.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Alsabti, K., Ranka, S., Singh, V.: An Efficient K-Means Clustering Algorithm. In: PPS/SPDP Workshop on High performance Data Mining (1997)
Alsabti, K., Ranka, S., Singh, V.: An Efficient Space-Partitioning Based Algorithm for the K-Means Clustering (1997)
Huang, Z.: Clustering Large Data Sets With Mixed Numeric and Categorical Values. In: Proceedings of The First Pacific-Asia Conference on Knowledge Discovery and Data Mining. World Scientific, Singapore (1997)
Huang, Z.: A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining. In: SIGMOD 1997 Data Mining Workshop (1997)
Keogh, E., Lin, J., Truppel, W.: Clustering of time series subsequences is meaningless: implications for previous and future research. In: ICDM 2003. Third IEEE International Conference on Data Mining, pp. 115–122 (2003)
Liu, W., Luo, Y.: Applications of clustering data mining in customer analysis in department store. In: Proceedings of ICSSSM 2005, International Conference on Services Systems and Services Management, pp. 1042–1046 (2005)
Chen, C.-H., Hong, T.-P., Tseng, V.S.: Analyzing time-series data by fuzzy data-mining technique. In: IEEE International Conference on Granular Computing, pp. 112–117 (2005)
Ozden, B., Ramaswamy, S., Silberschatz, A.: Cyclic Association Rules. In: Proc. of 1998 Int. Conf. Data Engineering (ICDE 1998), pp. 412–421 (1998)
Chen, X., Petrounias, I., Heathfield, H.: Discovering Temporal Association Rules in Temporal Databases. In: Proc. International Workshop on Issues and Applications of Database Technology (IADT 1998), pp. 312–319 (1998)
Ye, S., Keane, J.A.: Mining Association Rules in Temporal Databases. In: Proc. International Conference on Systems, Man and Cybernetics, pp. 2803–2808. IEEE, New York (1998)
Mannila, H., Toivonen, H., Verkamo, I.: Discovery of Frequent Episodes in Event Sequences. DMKD Journal 1(3), 259–289 (1997)
Oastes, T.: A family of Algorithms for Finding Temporal Structure in Data. In: 6th Workshop on AI and Statistics (1997)
Han, J., Dong, G., Yin, Y.: Efficient Mining of Partial Periodic Patterns in Time Series Database. In: Proc. Fifteenth International Conference on Data Engineering, Sydney, Australia, pp. 106–115. IEEE Computer Society Press, Los Alamitos (1999)
Parthasarathy, S., Zaki, M.J., Ogihara, M., Dwarkadas, S.: Incremental and Interactive Sequence Mining. In: CIKM 1999. ACM, New York (1999)
Patel, P., Keogh, E., Lin, J., Lonardi, S.: Mining motifs in massive time series databases. In: ICDM 2002, Proceedings of IEEE International Conference on Data Mining, pp. 370–377 (2002)
Povinelli, R.J., Feng, X.: A new temporal pattern identification method for characterization and prediction of complex time series events. IEEE Transactions on Knowledge and Data Engineering 15(2), 339–352 (2003)
Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057. Springer, Heidelberg (1996)
Wang, X.-Y., Wang, Z.-O.: Stock market time series data mining based on regularized neural network and rough set. In: Proceedings of 2002 International Conference on Machine Learning and Cybernetics, pp. 315–318 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hung, CL., Yang, DL., Chung, YC., Hung, MC. (2006). A Novel Mining Algorithm for Periodic Clustering Sequential Patterns. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_137
Download citation
DOI: https://doi.org/10.1007/11779568_137
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35453-6
Online ISBN: 978-3-540-35454-3
eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science