The field of Data Mining is widely recognized and accepted for its applications in many business problems to guide decision-making processes based on data. However, in recent times, the scope of these problems has swollen and the methods are under scrutiny for applicability and relevance to real-world circumstances. At the crossroads of innovation and standards, it is important to examine and understand whether the current theoretical methods for industrial applications (which include KDD, SEMMA and CRISP-DM) encompass all possible scenarios that could arise in practical situations.
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- Partial requirement for: M.S., Arizona State University, 2012Note typethesis
- Includes bibliographical references (p. 58-59)Note typebibliography
- Field of study: Computer science