Abstract
Traditional data mining is a data-driven trial-and-error process[1], which aims at discovered pattern/rule. People either view data mining as an autonomous process, or only analyze the issues in an isolated and case-by-case manner. Because it overlooks some valuable information, such as existing knowledge, expert experience, context and real constraints, the results coming out can’t be directly applied to support decisions in business. This paper proposes a new methodology called Data Mining Integrated With Domain Knowledge, aiming to discovery more interesting, more actionable knowledge.
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References
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© 2009 Springer-Verlag Berlin Heidelberg
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Huang, A., Zhang, L., Zhu, Z., Shi, Y. (2009). Data Mining Integrated with Domain Knowledge. In: Shi, Y., Wang, S., Peng, Y., Li, J., Zeng, Y. (eds) Cutting-Edge Research Topics on Multiple Criteria Decision Making. MCDM 2009. Communications in Computer and Information Science, vol 35. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02298-2_28
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DOI: https://doi.org/10.1007/978-3-642-02298-2_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02297-5
Online ISBN: 978-3-642-02298-2
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