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Data Mining Techniques for Churn Mitigation/Detection: Intrinsic Attributes Approach

Data Mining Techniques for Churn Mitigation/Detection: Intrinsic Attributes Approach

Copyright: © 2015 |Pages: 22
ISBN13: 9781466662889|ISBN10: 1466662883|EISBN13: 9781466662896
DOI: 10.4018/978-1-4666-6288-9.ch003
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MLA

Goran Klepac, et al. "Data Mining Techniques for Churn Mitigation/Detection: Intrinsic Attributes Approach." Developing Churn Models Using Data Mining Techniques and Social Network Analysis, IGI Global, 2015, pp.41-62. https://doi.org/10.4018/978-1-4666-6288-9.ch003

APA

G. Klepac, R. Kopal, & L. Mršić (2015). Data Mining Techniques for Churn Mitigation/Detection: Intrinsic Attributes Approach. IGI Global. https://doi.org/10.4018/978-1-4666-6288-9.ch003

Chicago

Goran Klepac, Robert Kopal, and Leo Mršić. "Data Mining Techniques for Churn Mitigation/Detection: Intrinsic Attributes Approach." In Developing Churn Models Using Data Mining Techniques and Social Network Analysis. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-6288-9.ch003

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Abstract

This chapter overviews data mining starting with an explanation of the data mining methods used the most. Data mining methods are explained together with recommendations of when and how to use them and how to iteratively combine different methods. The methods are explained briefly to understand their role in projects. One of the most important topics that the analysts (readers) have to learn is how to combine different methods in the same analysis and how to use that approach to unlock the synergy effect.

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