Abstract
Time is an important aspect of all real world phenomena. Any systems, approaches or techniques that are concerned with information need to take into account the temporal aspect of data. Data mining refers to a set of techniques for discovering previously unknown information from existing data in large databases and therefore, the information discovered will be of limited value if its temporal aspects, i.e. validity, periodicity, are not considered. This paper presents a generic definition of temporal patterns and a framework for discovering them. An architecture for the mining of such patterns is presented along with a temporal query language for extracting them from a database. As an instance of generic patterns, temporal association rules are used as examples of the proposed approach.
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© 1998 Springer-Verlag Berlin Heidelberg
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Chen, X., Petrounias, I. (1998). A framework for temporal data mining. In: Quirchmayr, G., Schweighofer, E., Bench-Capon, T.J. (eds) Database and Expert Systems Applications. DEXA 1998. Lecture Notes in Computer Science, vol 1460. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0054535
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DOI: https://doi.org/10.1007/BFb0054535
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