Euclidean Distance

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Euclidean Distanceraw, normalized, and double‐scaled coefficientsSeptember, 2005

f of 26Euclidean Distance – Raw, Normalised, and Double‐Scaled CoefficientsHaving been fiddling around with distance measures for some time – especially with regard to profilecomparison methodologies, I thought it was time I provided a brief and simple overview of EuclideanDistance – and why so many programs give so many completely different estimates of it. This is notbecause the concept itself changes (that of linear distance), but is due to the way programs/investigators either transform the data prior to computing the difference, normalise constituentdistances via a constant, or re‐scale the coefficient into a unit metric. However, few actually makeabsolutely explicit what they do, and the consequences of whatever transformation they undertake.Given that I always use a double‐scaling of distance into a unit metric for the coefficient, and nevertransform the raw data, I thought it time I explained the logic of this, and why I feel some of thecoefficients used within some popular statistical programs are sometimes less than optimal (i.e. using“normal z‐score” transformations).Raw Euclidean DistanceThe Euclidean metric (and distance magnitude) is that which corresponds to everyday experience andperceptions. That is, the kind of 1, 2, and 3‐Dimensional linear metric world where the distance betweenany two points in space corresponds to the length of a straight line drawn between them. Figure 1shows the scores of three individuals on two variables (Variable 1 is the x‐axis, Variable 2 the y‐axis) –Figure 1Person 1807050Variable 26040Person 1-3euclideandistancePerson 1-2euclideandistancePerson 2Person 2-3euclideandistancePerson 330202030Technical Whitepaper #6: Euclidean distance40Variable 160507080100September, 2005

f of 26The straight line between each “Person” is the Euclidean distance. There would this be three suchdistances to compute, one for each person‐to‐person distance.However, we could also calculate the Euclidean distance between the two variables, given the threeperson scores on each – as shown in Figure 2 Figure 2The Euclidean Distance between 2 variablesin the 3-person dimensional score spaceVariable 1Variable 2The formula for calculating the distance between each of the three individuals as shown in Figure 1 is:Eq. 1d v ( pi 11i p2i ) 2where the difference between two persons’ scores is taken, and squared, and summed for v variables (inour example v 2). Three such distances would be calculated, for p1 – p2, p1 – p3, and p2 ‐ p3.Technical Whitepaper #6: Euclidean distanceSeptember, 2005

f of 26The formula for calculating the distance between the two variables, given three persons scoring on eachas shown in Figure 1 is:d Eq. 2p (vi 11i v2i ) 2where the difference between two variables’ values is taken, and squared, and summed for p persons(in our example p 3). Only one distance would be computed – between v1 and v2.Let’s do the calculations for finding the Euclidean distances between the three persons, given theirscores on two variables. The data are provided in Table 1 below Table 11Var1Person 1Person 2Person 32030902Var2804440Using equation 1 d v ( pi 11i p2i ) 2For the distance between person 1 and 2, the calculation is:d (20 30) 2 (80 44) 2 37.36For the distance between person 1 and 3, the calculation is:d (20 90) 2 (80 40) 2 80.62For the distance between person 2 and 3, the calculation is:d (30 90) 2 (44 40) 2 60.13Technical Whitepaper #6: Euclidean distanceSeptember, 2005

f of 26Using equation 2, we can also calculate the distance between the two variables d p (vi 11i v2i ) 2d (20 80) 2 (30 44) 2 (90 40) 2 79.35Equation 1 is used where say we are comparing two “objects” across a range of variables – and trying todetermine how “dissimilar” the objects are (the Euclidean distance between the two objects taking intoaccount their magnitudes on the range of variables. These objects might be two person’s profiles, aperson and a target profile, in fact basically any two vectors taken across the same variables.Equation 2 is used where we are comparing two variables to one another – given a sample of pairedobservations on each (as we might with a pearson correlation), In our case above, the sample was threepersons.In both equations, Raw Euclidean Distance is being computed.Technical Whitepaper #6: Euclidean distanceSeptember, 2005

f of 26Normalised Euclidean DistanceThe problem with the raw distance coefficient is that it has no obvious bound value for the maximumdistance, merely one that says 0 absolute identity. Its range of values vary from 0 (absolute identity) tosome maximum possible discrepancy value which remains unknown until specifically computed. RawEuclidean distance varies as a function of the magnitudes of the observations. Basically, you don’t knowfrom its size whether a coefficient indicates a small or large distance.If I divided every person’s score by 10 in Table 1, and recomputed the euclidean distance between thepersons, I would now obtain distance values of 3.736 for person 1 compared to 2, instead of 37.36.Likewise, 8.06 for person 1 and 3, and 6.01 for persons 2 and 3. The raw distance conveys littleinformation about absolute dissimilarity.So, raw euclidean distance is acceptable only if relative ordering amongst a fixed set of profile attributesis required. But, even here, what does a figure of 37.36 actually convey. If the maximum possibleobservable distance is 38, then we know that the persons being compared are about as different as theycan be. But, if the maximum observable distance is 1000, then suddenly a value of 37.36 seems toindicate a pretty good degree of agreement between two persons.The fact of the matter is that unless we know the maximum possible values for a euclidean distance, wecan do little more than rank dissimilarities, without ever knowing whether any or them are actuallysimilar or not to one another in any absolute sense.A further problem is that raw Euclidean distance is sensitive to the scaling of each constituent variable.For example, comparing persons across variables whose score ranges are dramatically different.Likewise, when developing a matrix of Euclidean coefficients by comparing multiple variables to oneanother, and where those variables’ magnitude ranges are quite different.For example, say we have 10 variables and are comparing two person’s scores on them the variablescores might look like Table 21Person 1Var 1Var 2Var al Whitepaper #6: Euclidean distance2Person 22156130032538September, 2005

f of 26The two persons’ scores are virtually identical except for variable 5. The raw Euclidean distance for thesedata is: 100.03. If we had expressed the scores for variable 5 in the same metric as the other scores (ona 1‐10 metric scale), we would have scores of 1.2 and 1.3 respectively for each individual. The rawEuclidean distance is now: 2.65.Obviously, the question “is 2.65 good or bad” still exists – given we have no idea what the maximumpossible Euclidean distance might be for these data.This is where SYSTAT, Primer 5, and SPSS provide Standardization/Normalization options for the data soas to permit an investigator to compute a distance coefficient which is essentially “scale free”.Systat 10.2’s normalised Euclidean distance produces its “normalisation” by dividing each squareddiscrepancy between attributes or persons by the total number of squared discrepancies (or samplesize). ( p1i p2i ) 2 d vi 1 vEq. 3So, comparing two persons across their magnitudes on 10 variables, as in the Table 3 below,Table 31Person 1Var 1Var 2Var 3Var4Var5Var6Var7Var8Var9Var102Person 211461.232328Technical Whitepaper #6: Euclidean distance21561.332538September, 2005

f of 26We calculate 1 2 2 1 1 2 4 5 2 9 8 2 d . 0.837 10 10 10 10 For the data in Table 2, the SYSTAT normalized Euclidean distance would be 31.634Frankly, I can see little point in this standardization – as the final coefficient still remains scale‐sensitive.That is, it is impossible to know whether the value indicates high or low dissimilarity from the coefficientvalue alone.Technical Whitepaper #6: Euclidean distanceSeptember, 2005

f of 26Primer 5 – an ecological/marine biology softwarepackage allows the calculation of raw Euclideandistance as well as a normalized Euclidean distance.But, this normalization is problematic when just twovariables or persons are to be compared to oneanother and these are the only two persons orvariables in the dataset.An immediate problem is encountered when trying to analyse the data in Tables 2 or 3 causes an errormessageThis is due to the fact that Primer 5 is actually standardizing each row of data in the file hence, whentwo values are equal, as for variables 2, 4, 6, 7 etc., there is no variance, no standard deviation or it is setto zero, which then causes a division by zero in the standardization formula.I modified the data in Table 3 – to allow unequal values on each pair of variable scores for the twopersons Table 41Person 1Var 1Var 2Var 3Var4Var5Var6Var7Var8Var9Var102Person 211461.23232822551.3435373Person 1 - .707106781-0.7071067810.7071067814Person 2 - 67810.707106781-0.707106781What we see in columns 3 and 4 is what Primer 5 does with the data (by standardizing rows) It produces a normalized Euclidean distance calculation of 4.4721 for the data in columns 1 and 2. Theraw Euclidean distance is 3.4655If we change variable 5 to reflect the 1200 and 1300 values as in Table 2, the normalized Euclideandistance remains as 4.4721, whilst the raw coefficient is: 100.06. So, its normalization certainly ensuresstability of coefficient scaling given unequal metrics of the constituent variables, but the value itself isTechnical Whitepaper #6: Euclidean distanceSeptember, 2005

f of 26now a function of the number of variables. For example, if we had made the calculation over 500variables, the normalized Euclidean distance would be 31.627.The reason for this is because whatever the values of the variables for each individual, the standardizedvalues are always equal to 0.707106781 !Look at the following data in Table 5 below Table 51Person 1Var 1Var 2Var 3Var4Var5Var6Var7Var8Var9Var102Person son 1 - 707106781-0.7071067810.7071067814Person 2 - 067810.707106781-0.707106781The raw euclidean distance is 109780.23, the Primer 5 normalized coefficient remains at 4.4721.It’s clear that Primer 5 cannot provide a normalized Euclidean distance where just two objects are beingcompared across a range of attributes or samples. It seems to work only where more than two objectsexist in a data matrix, and more than two variables or samples are present. Then the standardizationpermits differentiation of values for samples or variables such that coefficients may be calculated. As adouble‐check – I added a 3rd person to the data of Table 5, shown in Table 6 Table 6EuclideanEuclidean Stats Corner document testdata fileStats Corner document test data file1Person 1Var 1Var 2Var 3Var4Var5Var6Var7Var8Var9Var1011461200323282Person 22255130043537Technical Whitepaper #6: Euclidean distance3Person 34442356100034562371Row Std Person 700541.154700542Row Std Person 0269-0.5773502693Row Std Person 7350269September, 2005

f of 26The Row Standardized values for each variable are shown as the last 3 variables.The normalised Euclidean distance between:Persons 1 and 2 is now 2.9304 (was 4.4721 when just two persons in the file)Persons 1 and 3 is 5.2268Persons 2 and 3 is 4.9085The actual raw data never changed – but the distance measure does, solely as a function of thetransformation.So, since on many occasions we might be trying to produce pair‐wise coefficients for ranking, directcomparisons, or profiling etc., it seems Primer 5 is just not built for these kinds of applications. Also, thenormalised distance measure itself will change as a function of how many objects there are to becompared in a data file – even though the actual data may remain completely the same for a subset ofthat file. The normalization by row is, at first glance, a sensible way of ensuring that each variable isexpressed in the same metric. But, it must not be forgotten that this is a data transformation. That is,you are no longer expressing Euclidean distance between the data at hand, but of derived, transformedvalues of that data.Technical Whitepaper #6: Euclidean distanceSeptember, 2005

f of 26SPSS permits a number of transformations of simple Euclidean distance – and allows both row“standardization” (normalisation in Primer 5) as well as “column” standardization. Not only that, but itallows for several other transformations of the data prior to computing a Euclidean distance. No formulaor examples are given of each in any SPSS documentation; the effect on a coefficient value is relative tothe particular transformation sought. For example, for the data file in Table 7,Table 7Euclidean Stats Corner document test data file1Person 1Var 1Var 2Var 3Var4Var5Var6Var7Var8Var9Var1011461200323282Person 22255130043537using the following SPSS Correlate‐Distances settings where the “variables” denote person 1 andperson 2 Technical Whitepaper #6: Euclidean distanceSeptember, 2005

f of 26we obtain a “Z‐score standardized data” Euclidean distance of 0. 008016. Here, the data have been“column standardized” prior to the calculation being made. If we instead seek a “by case” rowstandardization, we obtain a value of 4.4721 as per Primer 5.Other transformations produce other values with little rationale.Technical Whitepaper #6: Euclidean distanceSeptember, 2005

f of 26Double‐Scaled Euclidean DistanceWhen comparing two variables/persons, what we can do is to calculate the Euclidean distance from datawhich is transformed into a 0‐1 metric using a strictly linear method (rather than non‐linearnormalisation‐standardisation), then re‐scale the resultant Euclidean distance measure itself into a 0‐1range, scaling it into a range defined by 0 through to the maximum possible distance observablebetween the two variables/persons.Case 1: Comparing two variables or persons, where observations exists across many persons/cases.In the case of two variables whose minimum and maximum possible values are fixed (either by scalebounds or physical property characteristics) or when comparing say persons across variables, each ofwhose metric range differs, then each variable’s maximum observable discrepancy will need to becalculated and each constituent squared discrepancy of the Euclidean distance calculation will need tobe initially normalised using the particular maximum observable discrepancy. The resulting square rootof the sum of squared values represents the raw Euclidean distance of these normalised values, which inturn is then scaled to a metric between 0 and 1.0 (where 1.0 represents the maximum discrepancybetween the two variables’ scaled scores). Computationally, each squared discrepancy is transformedinto a 0‐1 range, using a simple linear conversion – we keep our raw data “as is” – and simply scale thesquared discrepancies for the variables into a 0 to 1.0 range.Computational steps Comparing two persons/objects across many variablesStep 1: Determine the maximum possible squared discrepancy for each variable comparison using theminimum and maximum values as specified. Call these values md. Each variable will possess a minimumand maximum so the md for each variable is just:mdi (Maximum for variable i – Minimum for variable i)2Step 2. Compute the sum of squared discrepancies per variable, dividing through the squareddiscrepancy (across persons) for each variable by the maximum possible discrepancy for that variable.Then take the square root of the sum to produce the scaled variable Euclidean distance.Given the formula in equation 1: d v ( pi 11i p2i ) 2it is now modified to reflect the operations in step 2 Eq. 3d1 ( p1i p2i ) 2 mdii 1 vwhere d1 the “scaled variable” Euclidean distancemdi the maximum possible squared discrepancy per variable i of v variables.Technical Whitepaper #6: Euclidean distanceSeptember, 2005

f 3. Compute the scaled value from step 3 by dividing it by15 of 26v , where v the number of variables. ( p1i p2i ) 2 mdii 1 d2 vvEq. 4Technical Whitepaper #6: Euclidean distanceSeptember, 2005

f of 26Example 1:Assume we have two persons on which we observe the following observations (Table 4) Euclidean Stats Corner document test data file12Person 1 Person 2Var 1Var 2Var 3537Given we have prior information which tells us that each variable possesses a fixed minimum of 0 and afixed maximum of 10, then the calculations are as follows:Step 1:Given any variable’s observations can possess 0 as the minimum, and 10 as the maximum, the maximumpossible observable squared discrepancy is thus (0‐10) 2 100 for each variable. These are our md’s.Step 2:Compute the sum of squared discrepancies per variable, dividing through the squared discrepancy(across persons) for each variable by the maximum possible discrepancy for that variable . these valuesare:Var 1Var 2Var 3Var4Var5Var6Var7Var8Var9Var10Euclidean Stats Corner document test data file3412SquaredScaled SquaredPerson 1 Person 01Technical Whitepaper #6: Euclidean distanceSeptember, 2005

f of 26Step 3:Sum the Scaled Squared Discrepancies, take the square root of the sum, and then scale this coefficientinto a 0‐1 metric by dividing the scaled Euclidean distance byd2 10 0.1201 0.1095910Note that if the minimum and maximum for each variable was between 0 – 25, then the double‐scaledEuclidean distance d2 0.043838. This is a reminder that the Euclidean distance is relative the maximumpossible distance between variables. This point is taken up again in the next example.Technical Whitepaper #6: Euclidean distanceSeptember, 2005

f of 26Example 2:Assume we have two persons on which we observe the following observations (Table 4) Euclidean Stats Corner document test data file12Person 1 Person 2Var 1Var 2Var 3537Howeve

The Euclidean metric (and distance magnitude) is that which corresponds to everyday experience and perceptions. That is, the kind of 1, 2, and 3‐Dimensional linear metric world where the distance between

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