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Distance/Similarity

PyCM's distance method provides users with a wide range of string distance/similarity metrics to evaluate a confusion matrix by measuring its distance to a perfect confusion matrix. Distance/Similarity metrics measure the distance between two vectors of numbers. Small distances between two objects indicate similarity. In the PyCM's distance method, a distance measure can be chosen from DistanceType. The measures' names are chosen based on the namig style suggested in [1].

from pycm import ConfusionMatrix, DistanceType

cm = ConfusionMatrix(matrix={0: {0: 3, 1: 0, 2: 0}, 1: {0: 0, 1: 1, 2: 2}, 2: {0: 2, 1: 1, 2: 3}})
$$TP \rightarrow True Positive$$$$TN \rightarrow True Negative$$$$FP \rightarrow False Positive$$$$FN \rightarrow False Negative$$$$POP \rightarrow Population$$

AMPLE

AMPLE similarity [2] [3].

$$sim_{AMPLE}=|\frac{TP}{TP+FP}-\frac{FN}{FN+TN}|$$ 
cm.distance(metric=DistanceType.AMPLE)

{0: 0.6, 1: 0.3, 2: 0.17142857142857143}
  • Notice : new in version 3.8

Anderberg's D

Anderberg's D [4].

$$sim_{Anderberg} = \frac{(max(TP,FP)+max(FN,TN)+max(TP,FN)+max(FP,TN))- (max(TP+FP,FP+TN)+max(TP+FP,FN+TN))}{2\times POP}$$ 
cm.distance(metric=DistanceType.Anderberg)

{0: 0.16666666666666666, 1: 0.0, 2: 0.041666666666666664}
  • Notice : new in version 3.8

Andres & Marzo's Delta

Andres & Marzo's Delta correlation [5].

$$corr_{AndresMarzo_\Delta} = \Delta = \frac{TP+TN-2 \times \sqrt{FP \times FN}}{POP}$$ 
cm.distance(metric=DistanceType.AndresMarzoDelta)

{0: 0.8333333333333334, 1: 0.5142977396044842, 2: 0.17508504286947035}
  • Notice : new in version 3.8

Baroni-Urbani & Buser I

Baroni-Urbani & Buser I similarity [6].

$$sim_{BaroniUrbaniBuserI} = \frac{\sqrt{TP\times TN}+TP}{\sqrt{TP\times TN}+TP+FP+FN}$$ 
cm.distance(metric=DistanceType.BaroniUrbaniBuserI)

{0: 0.79128784747792, 1: 0.5606601717798213, 2: 0.5638559245324765}
  • Notice : new in version 3.8

Baroni-Urbani & Buser II

Baroni-Urbani & Buser II correlation [6].

$$corr_{BaroniUrbaniBuserII} = \frac{\sqrt{TP \times TN}+TP-FP-FN}{\sqrt{TP \times TN}+TP+FP+FN}$$ 
cm.distance(metric=DistanceType.BaroniUrbaniBuserII)

{0: 0.58257569495584, 1: 0.12132034355964261, 2: 0.1277118490649528}
  • Notice : new in version 3.8

Batagelj & Bren

Batagelj & Bren distance [7].

$$dist_{BatageljBren} = \frac{FP \times FN}{TP \times TN}$$ 
cm.distance(metric=DistanceType.BatageljBren)

{0: 0.0, 1: 0.25, 2: 0.5}
  • Notice : new in version 3.8

Baulieu I

Baulieu I distance [8].

$$sim_{BaulieuI} = \frac{(TP+FP) \times (TP+FN)-TP^2}{(TP+FP) \times (TP+FN)}$$ 
cm.distance(metric=DistanceType.BaulieuI)

{0: 0.4, 1: 0.8333333333333334, 2: 0.7}
  • Notice : new in version 3.8

Baulieu II

Baulieu II similarity [8].

$$sim_{BaulieuII} = \frac{TP^2 \times TN^2}{(TP+FP) \times (TP+FN) \times (FP+TN) \times (FN+TN)}$$ 
cm.distance(metric=DistanceType.BaulieuII)

{0: 0.4666666666666667, 1: 0.11851851851851852, 2: 0.11428571428571428}
  • Notice : new in version 3.8

Baulieu III

Baulieu III distance [8].

$$sim_{BaulieuIII} = \frac{POP^2 - 4 \times (TP \times TN-FP \times FN)}{2 \times POP^2}$$ 
cm.distance(metric=DistanceType.BaulieuIII)

{0: 0.20833333333333334, 1: 0.4166666666666667, 2: 0.4166666666666667}
  • Notice : new in version 3.8

Baulieu IV

Baulieu IV distance [9].

$$dist_{BaulieuIV} = \frac{FP+FN-(TP+\frac{1}{2})\times(TN+\frac{1}{2})\times TN \times k}{POP}$$ 
cm.distance(metric=DistanceType.BaulieuIV)

{0: -41.45702383161246, 1: -22.855395541901885, 2: -13.85431293274332}
  • The default value of k is Euler's number $e$
  • Notice : new in version 3.8

Baulieu V

Baulieu V distance [9].

$$dist_{BaulieuV} = \frac{FP+FN+1}{TP+FP+FN+1}$$ 
cm.distance(metric=DistanceType.BaulieuV)

{0: 0.5, 1: 0.8, 2: 0.6666666666666666}
  • Notice : new in version 3.8

Baulieu VI

Baulieu VI distance [9].

$$dist_{BaulieuVI} = \frac{FP+FN}{TP+FP+FN+1}$$ 
cm.distance(metric=DistanceType.BaulieuVI)

{0: 0.3333333333333333, 1: 0.6, 2: 0.5555555555555556}
  • Notice : new in version 3.8

Baulieu VII

Baulieu VII distance [9].

$$dist_{BaulieuVII} = \frac{FP+FN}{POP + TP \times (TP-4)^2}$$ 
cm.distance(metric=DistanceType.BaulieuVII)

{0: 0.13333333333333333, 1: 0.14285714285714285, 2: 0.3333333333333333}
  • Notice : new in version 3.8

Baulieu VIII

Baulieu VIII distance [9].

$$dist_{BaulieuVIII} = \frac{(FP-FN)^2}{POP^2}$$ 
cm.distance(metric=DistanceType.BaulieuVIII)

{0: 0.027777777777777776, 1: 0.006944444444444444, 2: 0.006944444444444444}
  • Notice : new in version 3.8

Baulieu IX

Baulieu IX distance [9].

$$dist_{BaulieuIX} = \frac{FP+2 \times FN}{TP+FP+2 \times FN+TN}$$ 
cm.distance(metric=DistanceType.BaulieuIX)

{0: 0.16666666666666666, 1: 0.35714285714285715, 2: 0.5333333333333333}
  • Notice : new in version 3.8

Baulieu X

Baulieu X distance [9].

$$dist_{BaulieuX} = \frac{FP+FN+max(FP,FN)}{POP+max(FP,FN)}$$ 
cm.distance(metric=DistanceType.BaulieuX)

{0: 0.2857142857142857, 1: 0.35714285714285715, 2: 0.5333333333333333}
  • Notice : new in version 3.8

Baulieu XI

Baulieu XI distance [9].

$$dist_{BaulieuXI} = \frac{FP+FN}{FP+FN+TN}$$ 
cm.distance(metric=DistanceType.BaulieuXI)

{0: 0.2222222222222222, 1: 0.2727272727272727, 2: 0.5555555555555556}
  • Notice : new in version 3.8

Baulieu XII

Baulieu XII distance [9].

$$dist_{BaulieuXII} = \frac{FP+FN}{TP+FP+FN-1}$$ 
cm.distance(metric=DistanceType.BaulieuXII)

{0: 0.5, 1: 1.0, 2: 0.7142857142857143}
  • Notice : new in version 3.8

Baulieu XIII

Baulieu XIII distance [9].

$$dist_{BaulieuXIII} = \frac{FP+FN}{TP+FP+FN+TP \times (TP-4)^2}$$ 
cm.distance(metric=DistanceType.BaulieuXIII)

{0: 0.25, 1: 0.23076923076923078, 2: 0.45454545454545453}
  • Notice : new in version 3.8

Baulieu XIV

Baulieu XIV distance [9].

$$dist_{BaulieuXIV} = \frac{FP+2 \times FN}{TP+FP+2 \times FN}$$ 
cm.distance(metric=DistanceType.BaulieuXIV)

{0: 0.4, 1: 0.8333333333333334, 2: 0.7272727272727273}
  • Notice : new in version 3.8

Baulieu XV

Baulieu XV distance [9].

$$dist_{BaulieuXV} = \frac{FP+FN+max(FP, FN)}{TP+FP+FN+max(FP, FN)}$$ 
cm.distance(metric=DistanceType.BaulieuXV)

{0: 0.5714285714285714, 1: 0.8333333333333334, 2: 0.7272727272727273}
  • Notice : new in version 3.8

Benini I

Benini I correlation [10].

$$corr_{BeniniI} = \frac{TP \times TN-FP \times FN}{(TP+FN)\times(FN+TN)}$$ 
cm.distance(metric=DistanceType.BeniniI)

{0: 1.0, 1: 0.2, 2: 0.14285714285714285}
  • Notice : new in version 3.8

Benini II

Benini II correlation [10].

$$corr_{BeniniII} = \frac{TP \times TN-FP \times FN}{min((TP+FN)\times(FN+TN), (TP+FP)\times(FP+TN))}$$ 
cm.distance(metric=DistanceType.BeniniII)

{0: 1.0, 1: 0.3333333333333333, 2: 0.2}
  • Notice : new in version 3.8

Canberra

Canberra distance [11] [12].

$$sim_{Canberra} = \frac{FP+FN}{(TP+FP)+(TP+FN)}$$ 
cm.distance(metric=DistanceType.Canberra)

{0: 0.25, 1: 0.6, 2: 0.45454545454545453}
  • Notice : new in version 3.8

Clement

Clement similarity [13].

$$sim_{Clement} = \frac{TP}{TP+FP}\times\Big(1 - \frac{TP+FP}{POP}\Big) + \frac{TN}{FN+TN}\times\Big(1 - \frac{FN+TN}{POP}\Big)$$ 
cm.distance(metric=DistanceType.Clement)

{0: 0.7666666666666666, 1: 0.55, 2: 0.588095238095238}
  • Notice : new in version 3.8

Consonni & Todeschini I

Consonni & Todeschini I similarity [14].

$$sim_{ConsonniTodeschiniI} = \frac{log(1+TP+TN)}{log(1+POP)}$$ 
cm.distance(metric=DistanceType.ConsonniTodeschiniI)

{0: 0.9348704159880586, 1: 0.8977117175026231, 2: 0.8107144632819592}
  • Notice : new in version 3.8

Consonni & Todeschini II

Consonni & Todeschini II similarity [14].

$$sim_{ConsonniTodeschiniII} = \frac{log(1+POP)-log(1+FP+FN)}{log(1+POP)}$$ 
cm.distance(metric=DistanceType.ConsonniTodeschiniII)

{0: 0.5716826589686053, 1: 0.4595236911453605, 2: 0.3014445045412856}
  • Notice : new in version 3.8

Consonni & Todeschini III

Consonni & Todeschini III similarity [14].

$$sim_{ConsonniTodeschiniIII} = \frac{log(1+TP)}{log(1+POP)}$$ 
cm.distance(metric=DistanceType.ConsonniTodeschiniIII)

{0: 0.5404763088546395, 1: 0.27023815442731974, 2: 0.5404763088546395}
  • Notice : new in version 3.8

Consonni & Todeschini IV

Consonni & Todeschini IV similarity [14].

$$sim_{ConsonniTodeschiniIV} = \frac{log(1+TP)}{log(1+TP+FP+FN)}$$ 
cm.distance(metric=DistanceType.ConsonniTodeschiniIV)

{0: 0.7737056144690831, 1: 0.43067655807339306, 2: 0.6309297535714574}
  • Notice : new in version 3.8

Consonni & Todeschini V

Consonni & Todeschini V correlation [14].

$$corr_{ConsonniTodeschiniV} = \frac{log(1+TP \times TN)-log(1+FP \times FN)}{log(1+\frac{POP^2}{4})}$$ 
cm.distance(metric=DistanceType.ConsonniTodeschiniV)

{0: 0.8560267854703983, 1: 0.30424737289682985, 2: 0.17143541431350617}
  • Notice : new in version 3.8

Dennis

Dennis similarity [15].

$$sim_{Dennis} = \frac{TP-\frac{(TP+FP)\times(TP+FN)}{POP}}{\sqrt{\frac{(TP+FP)\times(TP+FN)}{POP}}}$$ 
cm.distance(metric=DistanceType.Dennis)

{0: 1.5652475842498528, 1: 0.7071067811865475, 2: 0.31622776601683794}
  • Notice : new in version 3.9

Digby

Digby correlation [16].

$$corr_{Digby} = \frac{(TP \times TN) ^\frac{3}{4}-(FP \times FN)^\frac{3}{4}}{(TP \times TN)^\frac{3}{4}+(FP \times FN)^\frac{3}{4}}$$ 
cm.distance(metric=DistanceType.Digby)

{0: 1.0, 1: 0.47759225007251715, 2: 0.2542302383508219}
  • Notice : new in version 3.9

Dispersion

Dispersion correlation [17].

$$corr_{dispersion} = \frac{TP \times TN -FP \times FN}{POP^2} $$ 
cm.distance(metric=DistanceType.Dispersion)

{0: 0.14583333333333334, 1: 0.041666666666666664, 2: 0.041666666666666664}
  • Notice : new in version 3.9

Doolittle

Doolittle similarity [18].

$$sim_{Doolittle} = \frac{(TP\times POP - (TP+FP)\times(TP+FN))^2}{(TP+FP)\times(TP+FN)\times(FP+TN)\times(FN+TN)}$$ 
cm.distance(metric=DistanceType.Doolittle)

{0: 0.4666666666666667, 1: 0.06666666666666667, 2: 0.02857142857142857}
  • Notice : new in version 3.9

Eyraud

Eyraud similarity [19].

$$sim_{Eyraud} = \frac{TP-(TP+FP)\times(TP+FN)}{(TP+FP)\times(TP+FN)\times(FP+TN)\times(FN+TN)}$$ 
cm.distance(metric=DistanceType.Eyraud)

{0: -0.012698412698412698, 1: -0.009259259259259259, 2: -0.02142857142857143}
  • Notice : new in version 3.9

Fager & McGowan

Fager & McGowan similarity [20] [21].

$$sim_{FagerMcGowan} = \frac{TP}{\sqrt{(TP+FP)\times(TP+FN)}} - \frac{1}{2\sqrt{max(TP+FP, TP+FN)}}$$ 
cm.distance(metric=DistanceType.FagerMcGowan)

{0: 0.5509898714915045, 1: 0.11957315586905015, 2: 0.3435984122732345}
  • Notice : new in version 3.9

Faith

Faith similarity [22].

$$sim_{Faith} = \frac{TP+\frac{TN}{2}}{POP}$$ 
cm.distance(metric=DistanceType.Faith)

{0: 0.5416666666666666, 1: 0.4166666666666667, 2: 0.4166666666666667}
  • Notice : new in version 3.9

Fleiss-Levin-Paik

Fleiss-Levin-Paik similarity [23].

$$sim_{FleissLevinPaik} = \frac{2 \times TN}{2 \times TN + FP + FN}$$ 
cm.distance(metric=DistanceType.FleissLevinPaik)

{0: 0.875, 1: 0.8421052631578947, 2: 0.6153846153846154}
  • Notice : new in version 3.9

Forbes I

Forbes I similarity [24] [25].

$$sim_{ForbesI} = \frac{POP \times TP}{(TP+FP)\times(TP+FN)}$$ 
cm.distance(metric=DistanceType.ForbesI)

{0: 2.4, 1: 2.0, 2: 1.2}
  • Notice : new in version 3.9

Forbes II

Forbes II correlation [26].

$$corr_{ForbesII} = \frac{FP \times FN-TP \times TN}{(TP+FP)\times(TP+FN) - POP \times min(TP+FP, TP+FN)}$$ 
cm.distance(metric=DistanceType.ForbesII)

{0: 1.0, 1: 0.3333333333333333, 2: 0.2}
  • Notice : new in version 3.9

Fossum

Fossum similarity [27].

$$sim_{Fossum} = \frac{POP \times (TP-\frac{1}{2})^2}{(TP+FP)\times(TP+FN)}$$ 
cm.distance(metric=DistanceType.Fossum)

{0: 5.0, 1: 0.5, 2: 2.5}
  • Notice : new in version 3.9

Gilbert & Wells

Gilbert & Wells similarity [28].

$$sim_{GilbertWells} = ln \frac{POP^3}{2\pi (TP+FP)\times(TP+FN)\times(FP+TN)\times(FN+TN)} + 2ln \frac{POP! \times TP! \times FP! \times FN! \times TN!}{(TP+FP)! \times (TP+FN)! \times (FP+TN)! \times (FN+TN)!}$$ 
cm.distance(metric=DistanceType.GilbertWells)

{0: 4.947742862177545, 1: 1.1129094954405283, 2: 0.4195337173255813}
  • Notice : new in version 3.9

Goodall

Goodall similarity [29] [30].

$$sim_{Goodall} =\frac{2}{\pi} \sin^{-1}\Big( \sqrt{\frac{TP + TN}{POP}} \Big)$$ 
cm.distance(metric=DistanceType.Goodall)

{0: 0.7322795271987701, 1: 0.6666666666666666, 2: 0.5533003790381138}
  • Notice : new in version 3.9

Goodman & Kruskal's Lambda

Goodman & Kruskal's Lambda similarity [31].

$$sim_{GK_\lambda} = \frac{\frac{1}{2}((max(TP,FP)+max(FN,TN)+max(TP,FN)+max(FP,TN))- (max(TP+FP,FN+TN)+max(TP+FN,FP+TN)))} {POP-\frac{1}{2}(max(TP+FP,FN+TN)+max(TP+FN,FP+TN))}$$ 
cm.distance(metric=DistanceType.GoodmanKruskalLambda)

{0: 0.5, 1: 0.0, 2: 0.09090909090909091}
  • Notice : new in version 3.9

Goodman & Kruskal Lambda-r

Goodman & Kruskal Lambda-r correlation [31].

$$corr_{GK_{\lambda_r}} = \frac{TP + TN - \frac{1}{2}(max(TP+FP,FN+TN)+max(TP+FN,FP+TN))} {POP - \frac{1}{2}(max(TP+FP,FN+TN)+max(TP+FN,FP+TN))} $$ 
cm.distance(metric=DistanceType.GoodmanKruskalLambdaR)

{0: 0.5, 1: -0.2, 2: 0.09090909090909091}
  • Notice : new in version 3.9

Guttman's Lambda A

Guttman's Lambda A similarity [32].

$$sim_{Guttman_{\lambda_a}} = \frac{max(TP, FN) + max(FP, TN) - max(TP+FP, FN+TN)}{POP - max(TP+FP, FN+TN)} $$ 
cm.distance(metric=DistanceType.GuttmanLambdaA)

{0: 0.6, 1: 0.0, 2: 0.0}
  • Notice : new in version 3.9

Guttman's Lambda B

Guttman's Lambda B similarity [32].

$$sim_{Guttman_{\lambda_b}} = \frac{max(TP, FP) + max(FN, TN) - max(TP+FN, FP+TN)}{POP - max(TP+FN, FP+TN)} $$ 
cm.distance(metric=DistanceType.GuttmanLambdaB)

{0: 0.3333333333333333, 1: 0.0, 2: 0.16666666666666666}
  • Notice : new in version 3.9

Hamann

Hamann correlation [33].

$$corr_{Hamann} = \frac{TP+TN-FP-FN}{POP} $$ 
cm.distance(metric=DistanceType.Hamann)

{0: 0.6666666666666666, 1: 0.5, 2: 0.16666666666666666}
  • Notice : new in version 3.9

Harris & Lahey

Harris & Lahey similarity [34].

$$sim_{HarrisLahey} = \frac{TP}{TP+FP+FN} \times \frac{2TN+FP+FN}{2POP}+ \frac{TN}{TN+FP+FN} \times \frac{2TP+FP+FN}{2POP} $$ 
cm.distance(metric=DistanceType.HarrisLahey)

{0: 0.6592592592592592, 1: 0.3494318181818182, 2: 0.4068287037037037}
  • Notice : new in version 3.9

Hawkins & Dotson

Hawkins & Dotson similarity [35].

$$sim_{HawkinsDotson} = \frac{1}{2} \times \Big(\frac{TP}{TP+FP+FN}+\frac{TN}{FP+FN+TN}\Big) $$ 
cm.distance(metric=DistanceType.HawkinsDotson)

{0: 0.6888888888888889, 1: 0.48863636363636365, 2: 0.4097222222222222}
  • Notice : new in version 3.9

Kendall's Tau

Kendall's Tau correlation [36].

$$corr_{KendallTau} = \frac{2 \times (TP+TN-FP-FN)}{POP \times (POP-1)} $$ 
cm.distance(metric=DistanceType.KendallTau)

{0: 0.12121212121212122, 1: 0.09090909090909091, 2: 0.030303030303030304}
  • Notice : new in version 3.9

Kent & Foster I

Kent & Foster I similarity [37].

$$sim_{KentFosterI} = \frac{TP-\frac{(TP+FP)\times(TP+FN)}{TP+FP+FN}}{TP-\frac{(TP+FP)\times(TP+FN)}{TP+FP+FN}+FP+FN} $$ 
cm.distance(metric=DistanceType.KentFosterI)

{0: 0.0, 1: -0.2, 2: -0.17647058823529413}
  • Notice : new in version 3.9

Kent & Foster II

Kent & Foster II similarity [37].

$$sim_{KentFosterII} = \frac{TN-\frac{(FP+TN)\times(FN+TN)}{FP+FN+TN}}{TN-\frac{(FP+TN)\times(FP+TN)}{FP+FN+TN}+FP+FN} $$ 
cm.distance(metric=DistanceType.KentFosterII)

{0: 0.0, 1: -0.06451612903225801, 2: -0.15384615384615394}
  • Notice : new in version 3.9

Köppen I

Köppen I correlation [38].

$$sim_{KoppenI} = \frac{\frac{2 \times TP+FP+FN}{2}.\frac{2 \times TN+FP+FN}{2} - \frac{FP+FN}{2}} {\frac{2 \times TP+FP+FN}{2}.\frac{2 \times TN+FP+FN}{2}} $$ 
cm.distance(metric=DistanceType.KoppenI)

{0: 0.96875, 1: 0.9368421052631579, 2: 0.9300699300699301}
  • Notice : new in version 4.1

Köppen II

Köppen II correlation [38].

$$sim_{KoppenII} = TP + \frac{FP + FN}{2} $$ 
cm.distance(metric=DistanceType.KoppenII)

{0: 4.0, 1: 2.5, 2: 5.5}
  • Notice : new in version 4.1

Kuder & Richardson

Kuder & Richardson correlation [39].

$$corr_{KuderRichardson} = \frac{4 \times (TP \times TN - FP \times FN)} {(TP+FP)(FN+TN) + (TP+FN)(FP+TN) + 2(TP \times TN - FP \times FN)} $$ 
cm.distance(metric=DistanceType.KuderRichardson)

{0: 0.8076923076923077, 1: 0.4067796610169492, 2: 0.2891566265060241}
  • Notice : new in version 4.1

Kuhns I

Kuhns I correlation [40].

$$corr_{KuhnsI} = \frac{2 \times \delta(TP + FP, TP + FN)} {N} $$$$ \delta(TP + FP, TP + FN) = TP - \frac{(TP + FP) \times (TP + FN)}{N} $$ 
cm.distance(metric=DistanceType.KuhnsI)

{0: 0.2916666666666667, 1: 0.08333333333333333, 2: 0.08333333333333333}
  • Notice : new in version 4.1

Kuhns II

Kuhns II correlation [40].

$$corr_{KuhnsII} = \frac{\delta(TP + FP, TP + FN)} {\max(TP + FP, TP + FN)} $$$$ \delta(TP + FP, TP + FN) = TP - \frac{(TP + FP) \times (TP + FN)}{N} $$ 
cm.distance(metric=DistanceType.KuhnsII)

{0: 0.35, 1: 0.16666666666666666, 2: 0.08333333333333333}
  • Notice : new in version 4.1

Kuhns III

Kuhns III correlation [40].

$$corr_{KuhnsIII} = \frac{\delta(TP + FP, TP + FN)} {(1-\frac{TP}{2 \times TP + FP + FN})(2 \times TP + FP + FN-\frac{(TP + FP)(TP + FN)}{N})} $$$$ \delta(TP + FP, TP + FN) = TP - \frac{(TP + FP) \times (TP + FN)}{N} $$ 
cm.distance(metric=DistanceType.KuhnsIII)

{0: 0.4148148148148148, 1: 0.1388888888888889, 2: 0.08088235294117647}
  • Notice : new in version 4.2

Kuhns IV

Kuhns IV correlation [40].

$$corr_{KuhnsIV} = \frac{\delta(TP + FP, TP + FN)} {\min(TP + FP, TP + FN)} $$$$ \delta(TP + FP, TP + FN) = TP - \frac{(TP + FP) \times (TP + FN)}{N} $$ 
cm.distance(metric=DistanceType.KuhnsIV)

{0: 0.5833333333333334, 1: 0.25, 2: 0.1}
  • Notice : new in version 4.2

Kuhns V

Kuhns V correlation [40].

$$corr_{KuhnsV} = \frac{\delta(TP + FP, TP + FN)} {\max((TP+FP)(1-\frac{TP+FP}{N}), (TP+FN)(1-\frac{TP+FN}{N}))} $$$$ \delta(TP + FP, TP + FN) = TP - \frac{(TP + FP) \times (TP + FN)}{N} $$ 
cm.distance(metric=DistanceType.KuhnsV)

{0: 0.6000000000000001, 1: 0.2222222222222222, 2: 0.16666666666666666}
  • Notice : new in version 4.2

Kuhns VI

Kuhns VI correlation [40].

$$corr_{KuhnsVI} = \frac{\delta(TP + FP, TP + FN)} {\min((TP+FP)(1-\frac{TP+FP}{N}), (TP+FN)(1-\frac{TP+FN}{N}))} $$$$ \delta(TP + FP, TP + FN) = TP - \frac{(TP + FP) \times (TP + FN)}{N} $$ 
cm.distance(metric=DistanceType.KuhnsVI)

{0: 0.7777777777777778, 1: 0.3, 2: 0.17142857142857146}
  • Notice : new in version 4.2

Kuhns VII

Kuhns VII correlation [40].

$$corr_{KuhnsVII} = \frac{\delta(TP + FP, TP + FN)} {\sqrt{(TP + FP) \times (TP + FN)}} $$$$ \delta(TP + FP, TP + FN) = TP - \frac{(TP + FP) \times (TP + FN)}{N} $$ 
cm.distance(metric=DistanceType.KuhnsVII)

{0: 0.45184805705753195, 1: 0.20412414523193154, 2: 0.09128709291752768}
  • Notice : new in version 4.2

References

1- C. C. Little, "Abydos Documentation," 2018.
2- V. Dallmeier, C. Lindig, and A. Zeller, "Lightweight defect localization for Java," in European conference on object-oriented programming, 2005: Springer, pp. 528-550.
3- R. Abreu, P. Zoeteweij, and A. J. Van Gemund, "An evaluation of similarity coefficients for software fault localization," in 2006 12th Pacific Rim International Symposium on Dependable Computing (PRDC'06), 2006: IEEE, pp. 39-46.
4- M. R. Anderberg, Cluster analysis for applications: probability and mathematical statistics: a series of monographs and textbooks. Academic press, 2014.
5- A. M. Andrés and P. F. Marzo, "Delta: A new measure of agreement between two raters," British journal of mathematical and statistical psychology, vol. 57, no. 1, pp. 1-19, 2004.
6- C. Baroni-Urbani and M. W. Buser, "Similarity of binary data," Systematic Zoology, vol. 25, no. 3, pp. 251-259, 1976.
7- V. Batagelj and M. Bren, "Comparing resemblance measures," Journal of classification, vol. 12, no. 1, pp. 73-90, 1995.
8- F. B. Baulieu, "A classification of presence/absence based dissimilarity coefficients," Journal of Classification, vol. 6, no. 1, pp. 233-246, 1989.
9- F. B. Baulieu, "Two variant axiom systems for presence/absence based dissimilarity coefficients," Journal of Classification, vol. 14, no. 1, pp. 0159-0170, 1997.
10- R. Benini, Principii di demografia. Barbera, 1901.
11- G. N. Lance and W. T. Williams, "Computer programs for hierarchical polythetic classification (“similarity analyses”)," The Computer Journal, vol. 9, no. 1, pp. 60-64, 1966.
12- G. N. Lance and W. T. Williams, "Mixed-Data Classificatory Programs I - Agglomerative Systems," Australian Computer Journal, vol. 1, no. 1, pp. 15-20, 1967.
13- P. W. Clement, "A formula for computing inter-observer agreement," Psychological Reports, vol. 39, no. 1, pp. 257-258, 1976.
14- V. Consonni and R. Todeschini, "New similarity coefficients for binary data," Match-Communications in Mathematical and Computer Chemistry, vol. 68, no. 2, p. 581, 2012.
15- S. F. Dennis, "The Construction of a Thesaurus Automatically From," in Statistical Association Methods for Mechanized Documentation: Symposium Proceedings, 1965, vol. 269: US Government Printing Office, p. 61.
16- P. G. Digby, "Approximating the tetrachoric correlation coefficient," Biometrics, pp. 753-757, 1983.
17- IBM Corp, "IBM SPSS Statistics Algorithms," ed: IBM Corp Armonk, NY, USA, 2017.
18- M. H. Doolittle, "The verification of predictions," Bulletin of the Philosophical Society of Washington, vol. 7, pp. 122-127, 1885.
19- H. Eyraud, "Les principes de la mesure des correlations," Ann. Univ. Lyon, III. Ser., Sect. A, vol. 1, no. 30-47, p. 111, 1936.
20- E. W. Fager, "Determination and analysis of recurrent groups," Ecology, vol. 38, no. 4, pp. 586-595, 1957.
21- E. W. Fager and J. A. McGowan, "Zooplankton Species Groups in the North Pacific: Co-occurrences of species can be used to derive groups whose members react similarly to water-mass types," Science, vol. 140, no. 3566, pp. 453-460, 1963.
22- D. P. Faith, "Asymmetric binary similarity measures," Oecologia, vol. 57, pp. 287-290, 1983.
23- J. L. Fleiss, B. Levin, and M. C. Paik, Statistical methods for rates and proportions. john wiley & sons, 2013.
24- S. A. Forbes, On the local distribution of certain Illinois fishes: an essay in statistical ecology. Illinois State Laboratory of Natural History, 1907.
25- A. Mozley, "The statistical analysis of the distribution of pond molluscs in western Canada," The American Naturalist, vol. 70, no. 728, pp. 237-244, 1936.
26- S. A. Forbes, "Method of determining and measuring the associative relations of species," Science, vol. 61, no. 1585, pp. 518-524, 1925.
27- E. G. Fossum and G. Kaskey, "Optimization and standardization of information retrieval language and systems," SPERRY RAND CORP PHILADELPHIA PA UNIVAC DIV, 1966.
28- N. Gilbert and T. C. Wells, "Analysis of quadrat data," The Journal of Ecology, pp. 675-685, 1966.
29- D. W. Goodall, "The distribution of the matching coefficient," Biometrics, pp. 647-656, 1967.
30- B. Austin and R. R. Colwell, "Evaluation of some coefficients for use in numerical taxonomy of microorganisms," International Journal of Systematic and Evolutionary Microbiology, vol. 27, no. 3, pp. 204-210, 1977.
31- L. A. Goodman, W. H. Kruskal, L. A. Goodman, and W. H. Kruskal, Measures of association for cross classifications. Springer, 1979.
32- L. Guttman, "An outline of the statistical theory of prediction," The prediction of personal adjustment, vol. 48, pp. 253-318, 1941.
33- U. Hamann, "Merkmalsbestand und verwandtschaftsbeziehungen der farinosae: ein beitrag zum system der monokotyledonen," Willdenowia, pp. 639-768, 1961.
34- F. C. Harris and B. B. Lahey, "A method for combining occurrence and nonoccurrence interobserver agreement scores," Journal of Applied Behavior Analysis, vol. 11, no. 4, pp. 523-527, 1978.
35- R. P. Hawkins and V. A. Dotson, "Reliability Scores That Delude: An Alice in Wonderland Trip Through the Misleading Characteristics of Inter-Observer Agreement Scores in Interval Recording," 1973.
36- M. G. Kendall, "A new measure of rank correlation," Biometrika, vol. 30, no. 1/2, pp. 81-93, 1938.
37- R. N. Kent and S. L. Foster, "Direct observational procedures: Methodological issues in naturalistic settings," Handbook of behavioral assessment, pp. 279-328, 1977.
38- W. Köppen, "In Repertorium für Meteorologie," Akademiia Nauk, pp. 189–238, 1870.
39- G. F. Kuder and M. W. Richardson, "The theory of the estimation of test reliability," Psychometrika, pp. 151–160, 1937.
40- J. L. Kuhns, "Statistical Association Methods for Mechanized Documentation," National Bureau of Standards Miscellaneous Publication, pp. 33-40, 1964.