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Does Matlab provide any facility for evaluating clustering methods? (cluster compactness and cluster separation. ....) Or is there any toolbox for it?
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The quality of a clustering result depends on both the similarity measure used by the method and its implementation. • The quality of a clustering method is also measured by its ability to discover some or all of the hidden patterns.
There are majorly two types of measures to assess the clustering performance. (i) Extrinsic Measures which require ground truth labels. Examples are Adjusted Rand index, Fowlkes-Mallows scores, Mutual information based scores, Homogeneity, Completeness and V-measure.
The cluster validity indices represent statistical functions used for quantitative evaluation of the clusters derived from a dataset. The objective is to determine the importance of the disclosed cluster structure produced by any clustering algorithm. In a recent review article [289] Xu et al.
Matlab provides Silhouette index and there is a toolbox CVAP: Cluster Validity Analysis Platform for Matlab. Which includes following validity indexes:
Note that you might need precompiled LIBRA binaries for your platform.
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Not in Matlab, but ELKI (Java) provides a dozen or so cluster quality measures for evaluation.
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