Methodologies of Data Mining

Compose a minimum 1,400-word analysis in which you include the following about Six Sigma for Data Mining:

• Appraise the popularity of Cross-Industry Standard Process for Data Mining (CRISP-DM), and evaluate its methodology.
• Construct an example of how Six Sigma for Data Mining helps manufacturing organizations.
• Evaluate the relation of the Define, Measure, Analyze, Improve and Control (DMAIC) methodology to Six Sigma for Data Mining.
• Investigate how Define, Measure, Analyze, Improve and Control (DMAIC) could be applied to a manufacturing organization.

Cite a minimum of 1 peer reviewed reference from the University Library and 1 reference from Chapter 3 of the textbook assigned for this course.

Format your assignment consistent with APA guidelines.

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Individual Assignment: Methodologies of Data Mining

Purpose of Assignment

To create predictive models that can be leveraged for targeting specific audiences, different techniques of data mining can be approached. Industry standard processes have been well documented and are currently in use by several organizations. This assignment will allow students to identify those models and understand their purpose the processes involved in using them.

Resources Required

REAL-WORLD DATA MINING: APPLIED BUSINESS ANALYTICS AND DECISION MAKING. Dursun Delen, (2015). Chapter 3: The Data Mining Process

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