Abstract
Naive measures of association between variables, such as linear correlation, are primarily sensitive to gross relationships, those patterns that are easy to detect, see, and describe. In prior chapters we examined measures that go beyond such naiveté and are able to detect more subtle dependencies between variables, in other words, anomalies in otherwise uncomplicated relationships. But what if we want a visual representation of the pattern that connects them? In this chapter we present several ways of doing this.
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© 2018 Timothy Masters
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Masters, T. (2018). Displaying Relationship Anomalies. In: Data Mining Algorithms in C++. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-3315-3_3
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DOI: https://doi.org/10.1007/978-1-4842-3315-3_3
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Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-3314-6
Online ISBN: 978-1-4842-3315-3
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