Applied multivariate techniques
Applied multivariate techniques
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Breaking the barrier of transactions: mining inter-transaction association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Visual Explorations in Finance
Visual Explorations in Finance
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Application of self-organizing maps to clustering of high-frequency financial data
ACSW Frontiers '04 Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Time series data vary with time. In the past, most of the researches focused on the matching of feature points or measuring of the similarities. They could successfully represent the feature patterns in a visualized way. In the mean while, those researches did not sufficiently describe the results in simple and understandable words. In this research, a two-phase architecture for mining time series data is introduced. By combining some different mining techniques, the difficulties mentioned above may be overcome. This architecture mainly consists of Exploratory Data Analysis (EDA) and techniques related to mining association rules. After the phase I analysis, quantitative association rules are obtained by phase II. Meanwhile, the rules of the architecture are able to be verified by accuracy analysis. Finally, a result of comparison with the traditional data mining techniques and this architecture shows that the two-phase architecture is superior to traditional techniques to the time series data.