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Toward an integrated knowledge discovery and data mining process model

Published online by Cambridge University Press:  01 March 2010

Sumana Sharma  and
Kweku-Muata Osei-Bryson
Sumana Sharma*
Affiliation:
Department of Information Systems, the Information Systems Research Institute, Virginia Commonwealth University, Richmond, VA 23284, USA
Kweku-Muata Osei-Bryson*
Affiliation:
Department of Information Systems, the Information Systems Research Institute, Virginia Commonwealth University, Richmond, VA 23284, USA
*
e-mail: Sharmas5@vcu.edu, KMOsei@.vcu.edu
e-mail: Sharmas5@vcu.edu, KMOsei@.vcu.edu
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Abstract

The knowledge discovery and data mining (KDDM) process models describe the various phases (e.g. business understanding, data understanding, data preparation, modeling, evaluation and deployment) of the KDDM process. They act as a roadmap for implementation of the KDDM process by presenting a list of tasks for executing the various phases. The checklist approach of describing the tasks is not adequately supported by appropriate tools, which specify ‘how’ the particular task can be implemented. This may result in tasks not being implemented. Another disadvantage is that the long checklist does not capture or leverage the dependencies that exist among the various tasks of the same and different phases. This not only makes the process cumbersome to implement, but also hinders possibilities for semi-automation of certain tasks. Given that each task in the process model serves an important goal and even affects the execution of related tasks due to the dependencies, these limitations are likely to negatively affect the efficiency and effectiveness of KDDM projects. This paper proposes an improved KDDM process model that overcomes these shortcomings by prescribing tools for supporting each task as well as identifying and leveraging dependencies among tasks for semi-automation of tasks, wherever possible.

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Type
Articles
Information
The Knowledge Engineering Review , Volume 25 , Issue 1 , March 2010 , pp. 49 - 67
Copyright
Copyright © Cambridge University Press 2010

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