CS 478 – Tools for Machine Learning and Data Mining Clustering Quality Evaluation.

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Presentation transcript:

CS 478 – Tools for Machine Learning and Data Mining Clustering Quality Evaluation

Quality Metrics External metrics – Computed from a comparison to actual clustering (or classification) labels. Internal metrics – Computed solely from the composition of a cluster, with no recourse to external information.

External Metrics – Setup (I) Let: be the target and computed clusterings, respectively. TC = CC = original set of data Define the following:

External Metrics – Setup (II) a: number of pairs of items that belong to the same cluster in both CC and TC b: number of pairs of items that belong to different clusters in both CC and TC c: number of pairs of items that belong to the same cluster in CC but different clusters in TC d: number of pairs of items that belong to the same cluster in TC but different clusters in CC

F-measure

Rand Index Measure of clustering agreement: how similar are these two ways of partitioning the data?

Adjusted Rand Index Extension of the Rand index that attempts to account for items that may have been clustered by chance

Average Entropy Measure of purity with respect to the target clustering

V-measure Entropy-based Combines: – Homogeneity (H): all computed clusters contain only items which are members of the same target cluster/class – Completeness (C): all data items of a given target cluster/class are in the same computed cluster

Internal Metrics Assume no target clustering Attempt to capture some intrinsic properties of the clustering – Distance-based Sum of all squares Diameter sum or maximum Sum of minimum distance between clusters, etc. – Probability-based E.g., KL divergence