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Decision Tree Inudction

Decision Tree Inudction

Roberta Siciliano, Claudio Conversano
Copyright: © 2005 |Pages: 6
ISBN13: 9781591405573|ISBN10: 1591405572|EISBN13: 9781591405597
DOI: 10.4018/978-1-59140-557-3.ch068
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MLA

Siciliano, Roberta, and Claudio Conversano. "Decision Tree Inudction." Encyclopedia of Data Warehousing and Mining, edited by John Wang, IGI Global, 2005, pp. 353-358. https://doi.org/10.4018/978-1-59140-557-3.ch068

APA

Siciliano, R. & Conversano, C. (2005). Decision Tree Inudction. In J. Wang (Ed.), Encyclopedia of Data Warehousing and Mining (pp. 353-358). IGI Global. https://doi.org/10.4018/978-1-59140-557-3.ch068

Chicago

Siciliano, Roberta, and Claudio Conversano. "Decision Tree Inudction." In Encyclopedia of Data Warehousing and Mining, edited by John Wang, 353-358. Hershey, PA: IGI Global, 2005. https://doi.org/10.4018/978-1-59140-557-3.ch068

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Abstract

Decision Tree Induction (DTI) is an important step of the segmentation methodology. It can be viewed as a tool for the analysis of large datasets characterized by high dimensionality and nonstandard structure. Segmentation follows a nonparametric approach, since no hypotheses are made on the variable distribution. The resulting model has the structure of a tree graph. It is considered a supervised method, since a response criterion variable is explained by a set of predictors.

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