Abstract
In this chapter, we introduce two simple but widely used methods: decision analysis and cluster analysis. Decision analysis is used to make decisions under an uncertain business environment. The simplest decision analysis method, known as a decision tree, is interpreted. Decision tree is simple but very powerful. In the latter half of this book, we use decision tree to analyze complicated product design and supply chain design problems.
Given a set of objects, cluster analysis is applied to find subsets, called clusters, which are similar and/or well separated. Cluster analysis requires similarity coefficients and clustering algorithms. In this chapter, we introduce a number of similarity coefficients and three simple clustering algorithms. In the second half of this book, we introduce how to apply cluster analysis to design complicated manufacturing problems.
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Yin, Y., Kaku, I., Tang, J., Zhu, J. (2011). Decision Analysis and Cluster Analysis. In: Data Mining. Decision Engineering. Springer, London. https://doi.org/10.1007/978-1-84996-338-1_1
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DOI: https://doi.org/10.1007/978-1-84996-338-1_1
Publisher Name: Springer, London
Print ISBN: 978-1-84996-337-4
Online ISBN: 978-1-84996-338-1
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