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Expanding Data Mining Theory for Industrial Applications

Abstract 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. Do the methods require changes or enhancements? As part of the thesis I study the current methods and delineate the ideas of these methods and illuminate their ... (more)
Created Date 2012
Contributor Anand, Aneeth (Author) / Liu, Huan (Advisor) / Kempf, Karl G (Advisor) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Subject Computer science / Computer engineering / CRISP-DM / Data Mining / KDD / SEMMA
Type Masters Thesis
Extent 99 pages
Language English
Reuse Permissions All Rights Reserved
Note M.S. Computer Science 2012
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis