Abstract
An automated data mining service offers an out-sourced, cost-effective analysis option for clients desiring to leverage their data resources for decision support and operational improvement. In the context of the service model, typically the client provides the service with data and other information likely to aid in the analysis process (e.g. domain knowledge, etc.). In return, the service provides analysis results to the client. We describe the required processes, issues, and challenges in automating the data mining and analysis process when the high-level goals are: (1) to provide the client with a high quality, pertinent analysis result; and (2) to automate the data mining service, minimizing the amount of human analyst effort required and the cost of delivering the service. We argue that by focusing on client problems within market sectors, both of these goals may be realized.
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Bradley, P.S. (2003). Data Mining as an Automated Service. In: Whang, KY., Jeon, J., Shim, K., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2003. Lecture Notes in Computer Science(), vol 2637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36175-8_1
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DOI: https://doi.org/10.1007/3-540-36175-8_1
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