@article{
 title = {Information Theoretic Measures for Clusterings Comparison: Is a Correction for Chance Necessary?},
 type = {article},
 websites = {http://delivery.acm.org/10.1145/1560000/1553511/p1073-vinh.pdf?ip=128.227.12.255&id=1553511&acc=ACTIVE%20SERVICE&key=5CC3CBFF4617FD07%2EC2A817F22E85290F%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&__acm__=1517859494_f78e1018d77795d4fdd37eb5f21670e6},
 id = {369d0773-d06c-3c79-b825-d6458831feca},
 created = {2018-02-05T19:33:23.546Z},
 accessed = {2018-02-05},
 file_attached = {true},
 profile_id = {371589bb-c770-37ff-8193-93c6f25ffeb1},
 group_id = {f982cd63-7ceb-3aa2-ac7e-a953963d6716},
 last_modified = {2018-02-05T19:33:26.048Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
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 abstract = {Information theoretic based measures form a fundamental class of similarity measures for comparing clusterings, beside the class of pair-counting based and set-matching based measures. In this paper, we discuss the ne-cessity of correction for chance for informa-tion theoretic based measures for clusterings comparison. We observe that the baseline for such measures, i.e. average value between random partitions of a data set, does not take on a constant value, and tends to have larger variation when the ratio between the num-ber of data points and the number of clus-ters is small. This effect is similar in some other non-information theoretic based mea-sures such as the well-known Rand Index. Assuming a hypergeometric model of ran-domness, we derive the analytical formula for the expected mutual information value be-tween a pair of clusterings, and then propose the adjusted version for several popular in-formation theoretic based measures. Some examples are given to demonstrate the need and usefulness of the adjusted measures.},
 bibtype = {article},
 author = {Vinh, Nguyen Xuan and Epps, Julien and Bailey, James}
}

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