October 2017
Beginner to intermediate
270 pages
7h
English
Content preview from Machine Learning for Developers
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Homogeneity, completeness, and V-measure
Homogeneity, completeness, and V-measure are three key related indicators of the quality of a clustering operation. In the following formulas, we will use K for the number of clusters, C for the number of classes, N for the total number of samples, and ack for the number of elements of class c in cluster k.
Homogeneity is a measure of the ratio of samples of a single class pertaining to a single cluster. The fewer different classes included in one cluster, the better. The lower bound should be 0.0 and the upper bound should be 1.0 (higher is better), and the formulation for it is expressed as follows:
Completeness measures the ratio of the member of a given class that is assigned to the same cluster: ...
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