References
Adamson GW, Boreham J (1974) The use of an association measure based on character structure to identify semantically related pairs of words and document titles. Inf Storage Retr 10(7–8):253–260
Alt H, Behrends B, Blömer J (1995) Approximate matching of polygonal shapes. Ann Math Artif Intell 13(3):251–265
Budescu DV (1980) Some new measures of profile dissimilarity. Appl Psychol Meas 4(2):261–272
Bueno De Mesquita B (1981) Risk, power distributions, and the likelihood of war. Int Stud Q 25(4):541–568
Can F, Ozkarahan EA (1985) Concepts of the cover coefficient-based clustering methodology. In: Proceedings of the 8th annual international ACM SIGIR conference on research and development in information retrieval, Montreal, pp 204–211
Cerra D, Datcu M (2012) A fast compression-based similarity measure with applications to content-based image retrieval. J Vis Commun Image Represent 23(2):293–302
Cilibrasi R, Vitanyi PMB (2007) The Google similarity distance. IEEE Trans Knowl Data Eng 19(3):370–383
Cilibrasi R, Vitányi PMB, Wolf R (2004) Algorithmic clustering of music based on string compression. Comput Music J 28(4):49–67
Cooper M, Foote J (2002) Automatic music summarization via similarity analysis. In: Proceedings of the IRCAM, Paris, pp 81–85
Cramér H (1999) Mathematical methods of statistics. Princeton University Press, Princeton
Deutch R, Cherner M, Grant I (2006) Significance of testing of a cluster of multivariate binary variables: comparison of the tripartite T index to three common similarity measures. Stat Methods Med Res 15:285–299
Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302. doi:10.2307/1932409
Eidenberger H (2006) Evaluation and analysis of similarity measures for content based visual information retrieval. Multimed Syst 12(2):71–87
Emond EJ, Mason DW (2002) A new rank correlation coefficient with application to the consensus ranking problem. J Multi-Criteria Decis Anal 11(1):17–28
Foote J (1999) Visualizing music and audio using self-similarity. In: Proceedings of the seventh ACM international conference on multimedia (part 1), Orlando, pp 77–80
Gonzalez RP, Cummings G, Mulekar MS, Rodning CB (2006) Increased mortality in rural vehicular trauma: identifying contributing factors through data linkage. J Trauma-Inj Infect Crit Care 61:404–409
Goodman LA, Kruskal WH (1954) Measures of association for cross classifications. Part I. J Am Stat Assoc 49:732–764
Goodman LA, Kruskal WH (1959) Measures of association for cross classifications. Part II. J Am Stat Assoc 52:123–163
Goodman LA, Kruskal WH (1963) Measures of association for cross classifications. Part III. J Am Statist Assoc 58:310–364
Hamming RW (1950) Error detecting and error correcting codes. Bell Syst Tech J 29(2):147–160
Heyer WR, Donnelly MA, McDiarmid RW, Hayek LC, Foster MS (1994) Measuring and monitoring biological diversity, chapter 9. Smithsonian Institution Press, Washington, DC
Iusi-Scarborough G (1988) Polarity, power, and risk in international disputes. J Confl Resolut 32(3):511–533
Jaccard P (1901) Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin de la Société Vaudoise des Sciences Naturelles 37:547–579
Jesorsky O, Kirchberg K, Frischholz R (2001) Robust face detection using the Hausdorff distance. In: Audio-and video-based biometric person authentication. Springer, Berlin, pp 90–95
Kårén O, Högberg N, Dahlberg A, Jonsson L, Nylund JE (1997) Inter and intraspecific variation in the ITS region of rDNA of ectomycorrhizal fungi in Fennoscandia as detected by endonuclease analysis. New Phytol 136(2):313–325
Kim CH (1991) Third-party participation in wars. J Confl Resolut 35(4):659–677
Kendall M (1948) Rank correlation methods. Charles Griffin, London
Keogh E, Lonardi S, Ratanamahatana CA (2004) Towards parameter free data mining. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, Seattle, pp 206–215
Krasnogor N, Pelta DA (2004) Measuring the similarity of protein structures by means of the universal similarity metric. Bioinformatics 20(7):1015–1021
Kuhn HW (1955) The Hungarian method for the assignment problem. Nav Res Logist Q 2:83–97
Levenshtein VI (1966) Binary codes capable of correcting deletions, insertions, and reversals. Sov Phys Dokl 10(8):707–710
Levina E, Bickel P (2001) The earth mover’s distance is the mallows distance: some insights from statistics. In: Proceedings of ICCV, Vancouver, pp 251–256
Li M, Badger JH, Chen X, Kwong S, Kearney P, Zhang H (2001) An information-based sequence distance and its application to whole mitochondrial genome phylogeny. Bioinformatics 17(2):149
Li M, Chen X, Li X, Ma B, Vitányi PMB (2004) The similarity metric. IEEE Trans Inf Theory 50(12):3250–3264
McGill M (1979) An evaluation of factors affecting document ranking by information retrieval systems. ERIC Record No. ED188567, Education Resources Information Center. http://eric.ed.gov
Moreno PJ, Ho PP, Vasconcelos N (2003) A Kullback-Leibler divergence based kernel for SVM classification in multimedia applications. Adv Neural Inf Process Syst 16:1385–1393
Mulekar MS, Boone JM, Aryal S (2010) Estimating sampling distributions of overlap coefficient and other similarity measures. In: Karian ZA, Dudewicz EJ (eds) Handbook of fitting distributions, chapter 25. CRC, Boca Raton, pp 1039–1090
Munkres J (1957) Algorithms for the assignment and transportation problems. J Soc Ind Appl Math 5(1):32–38
Rucklidge W (1996) Efficient visual recognition using the Hausdorff distance, vol 1173. Springer, Berlin
Shaw WM, Burgin R, Howell P (1997) Performance standards and evaluations in IR test collections: cluster-based retrieval models. Inf Process Manage 33(1):1–14
Signorino CS, Ritter JM (1999) Tau-b or not tau-b: measuring the similarity of foreign policy positions. Int Stud Q 43(1):115–144
Spearman C (1904) The proof and measurement of association between two things. Am J Psychol 15:72–101
Stoll RJ (1984) Bloc concentration and balance of power. J Confl Resol 28(1):25–50
Sweeney K, Keshk OMG (2005) The similarity of states: using S to compute dyadic interest similarity. Manage Peace Sci 22:165–187
Tulloss RE (1997) Assessment of similarity indices for undesirable properties and a new tripartite similarity index based on cost functions. In: Palm ME, Chapela IH (eds) Mycology in sustainable development: expanding concepts, vanishing borders. Parkway Publishers, Boon, pp 122–143
Tversky A (1977) Features of similarity. Psychol Rev 84(4):327–352
Willett P, Barnard JM, Downs GM (1998) Chemical similarity searching. J Chem Inf Comput Sci 38(6):983–996
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The authors appreciate help from Trey Lemley, Assistant Librarian, and Amy Prendergast, Senior Librarian, at the Biomedical Library of the University of South Alabama in locating some of the relevant articles referenced here.
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Mulekar, M.S., Brown, C.S. (2017). Distance and Similarity Measures. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7163-9_141-1
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