Abstract
Business information received from advanced data analysis and data mining is a critical success factor for companies wishing to maximize competitive advantage. The use of traditional tools and techniques to discover knowledge is ruthless and does not give the right information at the right time. Data mining should provide tactical insights to support the strategic directions. In this paper, we introduce a dynamic approach that uses knowledge discovered in previous episodes. The proposed approach is shown to be effective for solving problems related to the efficiency of handling database updates, accuracy of data mining results, gaining more knowledge and interpretation of the results, and performance. Our results do not depend on the approach used to generate itemsets. In our analysis, we have used an Apriori-like approach as a local procedure to generate large itemsets. We prove that the Dynamic Data Mining algorithm is correct and complete.
This research was supported in part by the U.S. Department of Energy, Grant No. DE-FG02- 97ER1220.
on leave from The Department of Computer Science and Automatic Control, Faculty of Engineering, Alexandria University, Alexandria, Egypt
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Raghavan, V., Hafez, A. (2000). Dynamic Data Mining. In: Logananthara, R., Palm, G., Ali, M. (eds) Intelligent Problem Solving. Methodologies and Approaches. IEA/AIE 2000. Lecture Notes in Computer Science(), vol 1821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45049-1_27
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DOI: https://doi.org/10.1007/3-540-45049-1_27
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