A novel mining algorithm for periodic clustering sequential patterns

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4031 LNAI, Issue , 2006, Pages 1299-1308

  Hung, Che Lun ^a   Yang, Don Lin ^b   Chung, Yeh Ching ^a   Hung, Ming Chuan ^b  

^a NATIONAL TSING HUA UNIVERSITY   (Taiwan)

^b FENG CHIA UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DECISION MAKING; INFORMATION ANALYSIS; PATIENT TREATMENT;

KNOWLEDGE DISCOVERY; PERIODIC CLUSTERING SEQUENTIAL (PCS) PATTERN; TIME SERIES DATA;

DATA MINING;

EID: 33746263297     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11779568_137     Document Type: Conference Paper

References (18)

1. K. Alsabti, S. Ranka, V. Singh, An Efficient K-Means Clustering Algorithm, PPS/SPDP Workshop on High performance Data Mining, 1997.
2. K. Alsabti, S. Ranka, V. Singh, An Efficient Space-Partitioning Based Algorithm for the K-Means Clustering, 1997.
3. Zhexue Huang, Clustering Large Data Sets With Mixed Numeric and Categorical Values, In Proceedings of The First Pacific-Asia Conference on Knowledge Discovery and Data Mining, Singapore, World Scientific, 1997.
4. Zhexue Huang, A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining, SIGMOD'97 Data Mining Workshop.
5. Keogh, E., Lin, J., Truppel, W, Clustering of time series subsequences is meaningless: implications for previous and future research. In Data Mining, 2003. ICDM 2003. Third IEEE International Conference, pp. 115-122.
6. Wencai Liu, Yu Luo, Applications of clustering data mining in customer analysis in department store, in Services Systems and Services Management, 2005. Proceedings of ICSSSM '05. 2005 International Conference, pp. 1042-1046.
7. Chun-Hao Chen, Tzung-Pei Hong, Tseng, V.S., Analyzing time-series data by fuzzy datamining technique. In Granular Computing, 2005 IEEE International Conference, pp. 112-117.
8. Ozden, B.; Ramaswamy, S.; and Silberschatz, A., Cyclic Association Rules. In Proc. Of 1998 Int. Conf. Data Engineering (ICDE'98), pp.412-421, 1998.
9. Chen, X., Petrounias, I. and Heathfield, H., Discovering Temporal Association Rules in Temporal Databases, In Proc. International Workshop on Issues and Applications of Database Technology (IADT'98), pp. 312-319, 1998.
10. Ye, S. and Keane, J.A., Mining Association Rules in Temporal Databases, In Proc. International Conference on Systems, Man and Cybernetics, IEEE, New York, pp.2803-8, 1998.
11. H. Mannila, H. Toivonen, and I. Verkamo, Discovery of Frequent Episodes in Event Sequences, DMKD Journal, 1(3): pp.259-289, 1997.
12. T. Oastes, A family of Algorithms for Finding Temporal Structure in Data, In 6th Workshop on AI and Statistics, 1997.
13. Han, J., Dong, G. and Yin, Y., Efficient Mining of Partial Periodic Patterns in Time Series Database. In Proc. Fifteenth International Conference on Data Engineering, Sydney, Australia. IEEE Computer Society, pp.106-115, 1999.
14. S. Parthasarathy, M. J. Zaki, M. Ogihara, S. Dwarkadas, Incremental and Interactive Sequence Mining, ACM, CKM'99, 1999.
15. Patel, P., Keogh, E., Lin, J., Lonardi, S., Mining motifs in massive time series databases, In Data Mining, 2002, ICDM 2002. Proceedings. 2002 IEEE International Conference, pp. 370-377.
16. Povinelll, R.J., Xin Feng, A new temporal pattern identification method for characterization and prediction of complex time series events, In 2003 Knowledge and Data Engineering, IEEE Transactions, Vol. 15, No. 2, pp. 339-352.
17. R. Srikant and R. Agrawal, Mining Sequential Patterns: Generalizations and Performance Improvements, In 5th EDBT, 1996.
18. Xiao-Ye Wang, Zheng-Ou Wang, Stock market time series data mining based on regularized neural network and rough set, In Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference, pp. 315-318.