Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
From rough set theory to evidence theory
Advances in the Dempster-Shafer theory of evidence
Elements of machine learning
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Foundations of Databases: The Logical Level
Foundations of Databases: The Logical Level
Mining Positive and Negative Knowledge in Clinical Databases Based on Rough Set Model
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Knowledge Discovery in Medical Multi-databases: A Rough Set Approach
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
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Since hospital information systems have been introduced in large hospitals, a large amount of data, including laboratory examinations, have been stored as temporal databases. The characteristics of these temporal databases are: (1) Each record are inhomogeneous with respect to time-series, including short-term effects and long-term effects. (2) Each record has more than 1000 attributes when a patient is followed for more than one year. (3) When a patient is admitted for a long time, a large amount of data is stored in a very short term. Even medical experts cannot deal with these large databases, the interest in mining some useful information from the data are growing. In this paper, we introduce a combination of extended moving average method, multiscale matching and rule induction method to discover new knowledge in medical temporal databases. This method was applied to a medical dataset, the results of which show that interesting knowledge is discovered from each database.