Abstract
Data mining, which aims at extracting interesting information from large collections of data, has been widely used as an active decision making tool. Real-world applications of data mining require a dynamic and resilient model that is aware of a wide variety of diverse and unpredictable contexts. Contexts consist of circumstantial aspects of the user and domain that may affect the data mining process. The underlying motivation is mining datasets in the presence of context factors may improve performance and efficacy of data mining as identifying the factors, which are not easily detectable with typical data mining techniques. This paper proposes a context-aware data mining framework, where context will (1) be represented in an ontology, (2) be automatically captured during data mining process, and (3) allow the adaptive behavior to carry over to powerful data mining. We have shown that the different behaviors and functionalities of our context-aware data mining framework dynamically generate information in dynamic, uncertain, and distributed medical applications.
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Singh, S., Vajirkar, P., Lee, Y. (2003). Context-Based Data Mining Using Ontologies. In: Song, IY., Liddle, S.W., Ling, TW., Scheuermann, P. (eds) Conceptual Modeling - ER 2003. ER 2003. Lecture Notes in Computer Science, vol 2813. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39648-2_32
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DOI: https://doi.org/10.1007/978-3-540-39648-2_32
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