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Building a Classification Tree

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Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

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Abstract

The basic data consists of a finite set E provided with a dissimilarity or a similarity function. The elements of E are of the same nature. As seen in Chap. 3, E can be a set of attributes, a set of objects or a set of categories. Tables 3.4 and 3.5 of Chap. 3 give a precise idea of the possible different versions of E. On the other hand, the set E may be weighted by a positive numerical measure \(\mu _{E}\), assigning to each of its elements x (\(x \in E\)) a weight \(\mu _{x}\). \(\mu _{x}\) defines the “importance” with which x has to be considered. The dissimilarity or similarity function on E is mostly numerical. However, it might be ordinal. In Chaps. 4–7, facets of building a similarity index or association coefficient on E have been minutely examined in relation to the descriptive nature of E.

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Notes

  1. 1.

    Two total preorders \(\omega \) and \(\omega '\) on a given set are comparable if the graph of one of both total preorders is included in the other one.

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  1. Department of Data Knowledge and Management, University of Rennes 1, IRISA, Rennes, Ille-et-Vilaine, France

    Israël César Lerman

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  1. Israël César Lerman
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Lerman, I.C. (2016). Building a Classification Tree. In: Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-4471-6793-8_10

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  • DOI: https://doi.org/10.1007/978-1-4471-6793-8_10

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