Abstract
We review similarity and distance measures used in Statistics for clustering and classification. We are motivated by the lack of most measures to adequately utilize a non uniform distribution defined on the data or sample space.
Such measures are mappings from O x O → R + where O is either a finite set of objects or vector space like R p and R + is the set of non-negative real numbers. In most cases those mappings fulfil conditions like symmetry and reflexivity. Moreover, further characteristics like transitivity or the triangle equation in case of distance measures are of concern.
We start with Hartigan’s list of proximity measures which he compiled in 1967. It is good practice to pay special attention to the type of scales of the variables involved, i.e. to nominal (often binary), ordinal and metric (interval and ratio) types of scales. We are interested in the algebraic structure of proximities as suggested by (1967) and (1971), information-theoretic measures as discussed by (1971), and the probabilistic W-distance measure as proposed by (1970). The last measure combines distances of objects or vectors with their corresponding probabilities to improve overall discrimination power. The idea is that rare events, i.e. set of values with a very low probability of observing, related to a pair of objects may be a strong hint to strong similarity of this pair.
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References
Borgelt, Ch., Prototype-based Classification and Clustering, Habilitationsschrift, Ottovon-Guericke-Universität Magdeburg, Magdeburg, 2005
Cormack, R.M., A review of classification (with Discussion), J.R.Stat. Soc., A, 31, 321–367
Cox, T.F. and Cox, M.A.A., Multidimensional Scaling, 2nd. Ed., Chapman & Hall, Boca Raton etc., 2001
Frakes, W.B. and Baeza-Yates, R., Information Retrieval: Data Structures and Algorithms, Prentice Hal, Upper Saddle River, 1992
Godan, M., Über die Komplexität der Bestimmung der Ähnlichkeit von geometrischen Objekten in höheren Dimensionen, Dissertation, Freie Universität Berlin, 1991
Gower, J., A general coefficient of similarity and some of its properties, Biometrics, 27, 857–874
Hartigan, J.A., Representation of similarity matrices by trees, J.Am.Stat.Assoc., 62, 1140–1158, 1967
Hubálek, Z., Coefficients of association and similarity based on binary (presence-absence) data; an evaluation, Biol. Rev., 57, 669–689
Kruse, R. and Meyer, K.D., Statistics with Vague Data. D. Reidel Publishing Company, Dordrecht, 1987
Kullback, S., Information Theory and Statistics, Wiley, New York etc., 1959
Jardine, N. and Sibson, R., Mathematical Taxonomy, Wiley, London, 1971
Mahalanobis, P.C., On the Generalized Distance in Statistics. In: Proceedings Natl. Inst. Sci. India, 2, 49–55, 1936
Murtagh, F., Identifying and Exploiting ultrametricity. In: Advances in Data Analysis, Decker, R. and Lenz, H.-J. (eds.), Springer, Heidelberg, 2007
Skarabis, H., Mathematische Grundlagen und praktische Aspekte der Diskrimination und Klassifikation, Physika-Verlag, Würzburg, 1970
Sneath, P.H.A. and Sokal, R.R., Numerical Taxonomy, Freeman and Co., San Francisco, 1973
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Lenz, HJ. (2008). Proximities in Statistics: Similarity and Distance. In: Della Riccia, G., Dubois, D., Kruse, R., Lenz, HJ. (eds) Preferences and Similarities. CISM International Centre for Mechanical Sciences, vol 504. Springer, Vienna. https://doi.org/10.1007/978-3-211-85432-7_6
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DOI: https://doi.org/10.1007/978-3-211-85432-7_6
Publisher Name: Springer, Vienna
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