Sørensen–Dice coefficient

The Sørensen–Dice coefficient (see below for other names) is a statistic used to gauge the similarity of two samples. It was independently developed by the botanists Thorvald Sørensen[1] and Lee Raymond Dice,[2] who published in 1948 and 1945 respectively.

Name

The index is known by several other names, especially Sørensen–Dice index,[3] Sørensen index and Dice's coefficient. Other variations include the "similarity coefficient" or "index", such as Dice similarity coefficient (DSC). Common alternate spellings for Sørensen are Sorenson, Soerenson and Sörenson, and all three can also be seen with the –sen ending.

Other names include:

Formula

Sørensen's original formula was intended to be applied to discrete data. Given two sets, X and Y, it is defined as

where |X| and |Y| are the cardinalities of the two sets (i.e. the number of elements in each set). The Sørensen index equals twice the number of elements common to both sets divided by the sum of the number of elements in each set.

When applied to Boolean data, using the definition of true positive (TP), false positive (FP), and false negative (FN), it can be written as

.

It is different from the Jaccard index which only counts true positives once in both the numerator and denominator. DSC is the quotient of similarity and ranges between 0 and 1.[9] It can be viewed as a similarity measure over sets.

Similarly to the Jaccard index, the set operations can be expressed in terms of vector operations over binary vectors a and b:

which gives the same outcome over binary vectors and also gives a more general similarity metric over vectors in general terms.

For sets X and Y of keywords used in information retrieval, the coefficient may be defined as twice the shared information (intersection) over the sum of cardinalities :[10]

When taken as a string similarity measure, the coefficient may be calculated for two strings, x and y using bigrams as follows:[11]

where nt is the number of character bigrams found in both strings, nx is the number of bigrams in string x and ny is the number of bigrams in string y. For example, to calculate the similarity between:

night
nacht

We would find the set of bigrams in each word:

{ni,ig,gh,ht}
{na,ac,ch,ht}

Each set has four elements, and the intersection of these two sets has only one element: ht.

Inserting these numbers into the formula, we calculate, s = (2 · 1) / (4 + 4) = 0.25.

Difference from Jaccard

This coefficient is not very different in form from the Jaccard index. In fact, both are equivalent in the sense that given a value for the Sørensen–Dice coefficient , one can calculate the respective Jaccard index value and vice versa, using the equations and .

Since the Sørensen–Dice coefficient does not satisfy the triangle inequality, it can be considered a semimetric version of the Jaccard index.[4]

The function ranges between zero and one, like Jaccard. Unlike Jaccard, the corresponding difference function

is not a proper distance metric as it does not satisfy the triangle inequality.[4] The simplest counterexample of this is given by the three sets {a}, {b}, and {a,b}, the distance between the first two being 1, and the difference between the third and each of the others being one-third. To satisfy the triangle inequality, the sum of any two of these three sides must be greater than or equal to the remaining side. However, the distance between {a} and {a,b} plus the distance between {b} and {a,b} equals 2/3 and is therefore less than the distance between {a} and {b} which is 1.

Applications

The Sørensen–Dice coefficient is useful for ecological community data (e.g. Looman & Campbell, 1960[12]). Justification for its use is primarily empirical rather than theoretical (although it can be justified theoretically as the intersection of two fuzzy sets[13]). As compared to Euclidean distance, the Sørensen distance retains sensitivity in more heterogeneous data sets and gives less weight to outliers.[14] Recently the Dice score (and its variations, e.g. logDice taking a logarithm of it) has become popular in computer lexicography for measuring the lexical association score of two given words.[15] logDice is also used as part of the Mash Distance for genome and metagenome distance estimation[16] Finally, Dice is used in image segmentation, in particular for comparing algorithm output against reference masks in medical applications.[8]

Abundance version

The expression is easily extended to abundance instead of presence/absence of species. This quantitative version is known by several names:

See also

References
  1. Sørensen, T. (1948). "A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons". Kongelige Danske Videnskabernes Selskab. 5 (4): 1–34.
  2. Dice, Lee R. (1945). "Measures of the Amount of Ecologic Association Between Species". Ecology. 26 (3): 297–302. doi:10.2307/1932409. JSTOR 1932409.
  3. Carass, A.; Roy, S.; Gherman, A.; Reinhold, J.C.; Jesson, A.; et al. (2020). "Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis". Scientific Reports. 10 (1): 8242. Bibcode:2020NatSR..10.8242C. doi:10.1038/s41598-020-64803-w. ISSN 2045-2322. PMC 7237671. PMID 32427874.
  4. Gallagher, E.D., 1999. COMPAH Documentation, University of Massachusetts, Boston
  5. Nei, M.; Li, W.H. (1979). "Mathematical model for studying genetic variation in terms of restriction endonucleases". PNAS. 76 (10): 5269–5273. Bibcode:1979PNAS...76.5269N. doi:10.1073/pnas.76.10.5269. PMC 413122. PMID 291943.
  6. Prescott, J.W.; Pennell, M.; Best, T.M.; Swanson, M.S.; Haq, F.; Jackson, R.; Gurcan, M.N. (2009). An automated method to segment the femur for osteoarthritis research. IEEE. doi:10.1109/iembs.2009.5333257. PMC 2826829.
  7. Swanson, M.S.; Prescott, J.W.; Best, T.M.; Powell, K.; Jackson, R.D.; Haq, F.; Gurcan, M.N. (2010). "Semi-automated segmentation to assess the lateral meniscus in normal and osteoarthritic knees". Osteoarthritis and Cartilage. 18 (3): 344–353. doi:10.1016/j.joca.2009.10.004. ISSN 1063-4584. PMC 2826568. PMID 19857510.
  8. Zijdenbos, A.P.; Dawant, B.M.; Margolin, R.A.; Palmer, A.C. (1994). "Morphometric analysis of white matter lesions in MR images: method and validation". IEEE Transactions on Medical Imaging. 13 (4): 716–724. doi:10.1109/42.363096. ISSN 0278-0062. PMID 18218550.
  9. http://www.sekj.org/PDF/anbf40/anbf40-415.pdf
  10. van Rijsbergen, Cornelis Joost (1979). Information Retrieval. London: Butterworths. ISBN 3-642-12274-4.
  11. Kondrak, Grzegorz; Marcu, Daniel; Knight, Kevin (2003). "Cognates Can Improve Statistical Translation Models" (PDF). Proceedings of HLT-NAACL 2003: Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics. pp. 46–48.
  12. Looman, J.; Campbell, J.B. (1960). "Adaptation of Sorensen's K (1948) for estimating unit affinities in prairie vegetation". Ecology. 41 (3): 409–416. doi:10.2307/1933315. JSTOR 1933315.
  13. Roberts, D.W. (1986). "Ordination on the basis of fuzzy set theory". Vegetatio. 66 (3): 123–131. doi:10.1007/BF00039905. S2CID 12573576.
  14. McCune, Bruce & Grace, James (2002) Analysis of Ecological Communities. Mjm Software Design; ISBN 0-9721290-0-6.
  15. Rychlý, P. (2008) A lexicographer-friendly association score. Proceedings of the Second Workshop on Recent Advances in Slavonic Natural Language Processing RASLAN 2008: 6–9
  16. Ondov, Brian D., et al. "Mash: fast genome and metagenome distance estimation using MinHash." Genome biology 17.1 (2016): 1-14.
  17. Bray, J. Roger; Curtis, J. T. (1957). "An Ordination of the Upland Forest Communities of Southern Wisconsin". Ecological Monographs. 27 (4): 326–349. doi:10.2307/1942268. JSTOR 1942268.
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