Abstract
This paper discusses the use of an association rule algorithm in data mining and the processes of handling missing data in a distributed database environment. The investigation generated improved association rules using the model described here. The evaluations showed that more association patterns were generated in which the algorithm for missing data was used; this suggested more rules generated than by simply ignoring them. This implies that the model offer more precise and important association rules that is more valuable when applied for business decision making. With the discovery of accurate association rules or business patterns, approach for better market plans can be prepared and implemented to improve marketing schemes. One best-related application of handling missing data is for detecting fraud or devious database entries.
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© 2004 Springer-Verlag Berlin Heidelberg
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Gerardo, B.D., Lee, J., Lee, J., Park, M., Lee, M. (2004). The Association Rule Algorithm with Missing Data in Data Mining. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds) Computational Science and Its Applications – ICCSA 2004. ICCSA 2004. Lecture Notes in Computer Science, vol 3043. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24707-4_13
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DOI: https://doi.org/10.1007/978-3-540-24707-4_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22054-1
Online ISBN: 978-3-540-24707-4
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