On the Indeterminacy of Resemblance Measures for Binary (Presence/Absence) Data

Many similarity coefficients for binary data are defined as fractions. For certain resemblance measures the denominator may become zero. If the denominator is zero the value of the coefficient is indeterminate. It is shown that the seriousness of the indeterminacy problem differs with the resemblance measures. Following Batagelj and Bren (1995) we remove the indeterminacies by defining appropriate values in critical cases. The author would like to thank three anonymous reviewers for their helpful comments and valuable suggestions on earlier versions of this article.     

<Emphasis Type="Italic">k</Emphasis>-Adic Similarity Coefficients for Binary (Presence/Absence) Data

k-Adic formulations (for groups of objects of size k) of a variety of 2-adic similarity coefficients (for pairs of objects) for binary (presence/absence) data are presented. The formulations are not functions of 2-adic similarity coefficients. Instead, the main objective of the the paper is to present k-adic formulations that reflect certain basic characteristics of, and have a similar interpretation as, their 2-adic versions. Two major classes are distinguished. The first class is referred to as Bennani-Heiser similarity coefficients, which contains all coefficients that can be defined using just the matches, the number of attributes that are present and that are absent in k objects, and the total number of attributes. The coefficients in the second class can be formulated as functions of Dice’s association indices. The author thanks Willem Heiser and three anonymous reviewers for their helpful comments and valuable suggestions on earlier versions of this article.