Incremental and interactive sequence mining
Proceedings of the eighth international conference on Information and knowledge management
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Mining Motifs in Massive Time Series Databases
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
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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.