Algorithms for clustering data
Algorithms for clustering data
Comparison of Chernoff-type face and non-graphical methods for clustering multivariate observations
Computational Statistics & Data Analysis
MCS: A Method for Finding the Number of Clusters
Journal of Classification
Dissimilarity and similarity measures for comparing dendrograms and their applications
Advances in Data Analysis and Classification
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Correcting a similarity index for chance agreement requires computing its expectation under fixed marginal totals of a matching counts matrix. For some indices, such as Jaccard, Rogers and Tanimoto, Sokal and Sneath, and Gower and Legendre the expectations cannot be easily found. We show how such similarity indices can be expressed as functions of other indices and expectations found by approximations such that approximate correction is possible. A second approach is based on Taylor series expansion. A simulation study illustrates the effectiveness of the resulting correction of similarity indices using structured and unstructured data generated from bivariate normal distributions.