Measures of similarity and dissimilarityCalve, LeGordon, A. D., ‖2 Measures of similarity and dissimilarity‖, Classification. 2nd ed. Chapman &H all, pp.15-34, 1999.
The basis of many measures of similarity and dissimilarity is euclidean distance. The distance between vectors X and Y is defined as follows: In other words, euclidean distance is the square root of the sum of squared differences between corresponding elements of the two vectors. Note that the ...
In addition to the existing similarity and dissimilarity measures, 3 new similarity measures and 1 new dissimilarity measure are introduced. The performances of 16 similarity measures and 10 dissimilarity measures in image matching are determined and compared, and their sensitivities to noise and ...
Similarity and dissimilarity measures between fuzzy sets: a formal relational study Inf. Sci. (2013) B. De Baets et al. Transitivity-preserving fuzzification schemes for cardinality-based similarity measures Eur. J. Oper. Res. (2005) B. De Baets et al. On the transitivity of a parametric fam...
The element in the ith row and jth column gives either the similarity or dissimilarity between the ith and jth observation (or variable). Whether you get a similarity or a dissimilarity depends upon the requested measure; see [MV] measure option. If the number of observations (or variables) ...
Measuring molecular similarity or dissimilarity has two basic components: the representation of molecular characteristics (such as fingerprints) and the similarity coefficient that is used to quantify the degree of resemblance between two such representations. ...
Many numerical indices which quantify the similarity and dissimilarity between a pair of stringsX and Y, have been defined in the literature. Some of these include the Length of their Longest Common Subsequence (LLCS(X, Y)), the Length of their Shortest Common Supersequence (LSCS(X, Y)), ...
Design of similarity and dissimilarity measures for fuzzy sets on the basis of distance measure International Journal of Fuzzy Systems, 11 (2) (2009), pp. 67-72 View in ScopusGoogle Scholar [5] K. Atanassov Intuitionistic fuzzy sets Fuzzy Sets and Systems, 20 (1986), pp. 87-96 View PDF...
correlation based dissimilarity measures; difference-based dissimilarity measures; dissimilarity-based clustering algorithms; missing attribute values Summary This chapter presents a selection of the most commonly used general-purpose similarity and dissimilarity measures for clustering, providing a necessary common...
The subject of this chapter is image similarity measures. These measure provide a quantitative measure of the degree of match between two images, or image patches, A and B. Image similarity measures play an important role in many image fusion algorithms and applications including retrieval, classifi...