Adjusted for chance measures are widely used to compare
partitions/clusterings of the same data set. In particular, the Adjusted Rand
Index (ARI) based on pair-counting, and the Adjusted Mutual Information (AMI)
based on Shannon information theory are very popular in the clustering
community. Nonetheless it is an open problem as to what are the best
application scenarios for each measure and guidelines in the literature for
their usage are sparse, with the result that users often resort to using both.
Generalized Information Theoretic (IT) measures based on the Tsallis entropy
have been shown to link pair-counting and Shannon IT measures. In this paper,
we aim to bridge the gap between adjustment of measures based on pair-counting
and measures based on information theory. We solve the key technical challenge
of analytically computing the expected value and variance of generalized IT
measures. This allows us to propose adjustments of generalized IT measures,
which reduce to well known adjusted clustering comparison measures as special
cases. Using the theory of generalized IT measures, we are able to propose the
following guidelines for using ARI and AMI as external validation indices: ARI
should be used when the reference clustering has large equal sized clusters;
AMI should be used when the reference clustering is unbalanced and there exist
small clusters.

本文介绍基于 Tsallis 熵的广义信息熵测量值计算方法，并将其应用于聚类比较中，从而提供了 AR1 和 AMI 的最佳应用场景和使用指南。