Proximity Matrix
Here is the output of an SPSS distance matrix. The matrix is symmetric, meaning that the numbers on the lower half will be the same as the numbers in the top half. Quite often only the lower half of a symmetric matrix is displayed, with other information being displayed in the upper half (such as a combination between distances and correlation coefficients).
I've highlighted two numbers in this matrix. In green is the distance between the 2 closest cells (2 & 19) in 7 dimensional space (each dimension representing a different variable). In yellow are the 2 cells that are farthest apart (19 & 23).
This is useful in explaining something else as well. Cells 2 & 19 are close together, but 19 & 23 are far apart. You can imagine then that because 2 & 19 are clustered together in space 2 must also be far from 23. Looking at the matrix this shows that indeed 2 & 23 are relatively far apart.
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 0 | 1.273091 | 1.12288 | 1.008626 | 1.131625 | 1.230302 | 0.668893 | 1.06268 | 1.299697 | 1.18704 | 1.23804 | 1.299576 | 1.196235 | 0.618855 | 0.665795 | 0.54746 | 1.158025 | 1.278158 | 1.27371 | 1.075207 | 0.531415 | 0.701549 | 1.223166 | 1.182887 | 0.956577 | 1.063487 | 1.110752 | 1.290979 | 1.187189 | 1.19892 |
2 | 1.273091 | 0 | 0.980597 | 0.840149 | 0.928302 | 0.891628 | 1.142516 | 0.866625 | 1.259231 | 0.504748 | 1.125121 | 0.371013 | 0.760708 | 1.141332 | 0.955521 | 1.209867 | 0.862577 | 0.444649 | 0.188373 | 0.643415 | 0.995657 | 1.125258 | 1.74038 | 1.479599 | 1.465247 | 1.493416 | 1.522715 | 1.422669 | 1.487738 | 1.189664 |
3 | 1.12288 | 0.980597 | 0 | 0.500501 | 0.376001 | 0.362472 | 1.082317 | 0.265972 | 0.902611 | 0.892251 | 0.599579 | 1.002578 | 0.560964 | 1.050841 | 1.017548 | 1.165269 | 0.287241 | 0.931873 | 1.030943 | 0.631289 | 0.927306 | 0.978781 | 1.272868 | 1.113743 | 1.116642 | 1.023618 | 1.06467 | 0.846439 | 0.851927 | 0.689503 |
4 | 1.008626 | 0.840149 | 0.500501 | 0 | 0.512498 | 0.558006 | 1.001508 | 0.315122 | 0.701318 | 0.783516 | 0.549547 | 0.88278 | 0.540252 | 0.859854 | 0.918177 | 0.910809 | 0.36856 | 0.797291 | 0.879939 | 0.41629 | 0.739009 | 0.781714 | 1.400413 | 1.215507 | 1.097629 | 1.10128 | 1.161674 | 1.075774 | 0.989285 | 0.808469 |
5 | 1.131625 | 0.928302 | 0.376001 | 0.512498 | 0 | 0.436753 | 1.257965 | 0.382349 | 0.739377 | 0.861793 | 0.449417 | 0.827537 | 0.500326 | 1.077903 | 1.143161 | 1.185613 | 0.493578 | 0.779071 | 0.964978 | 0.449985 | 0.95813 | 0.925659 | 1.307955 | 1.069247 | 1.017394 | 1.095686 | 1.20998 | 0.996923 | 1.034318 | 0.785933 |
6 | 1.230302 | 0.891628 | 0.362472 | 0.558006 | 0.436753 | 0 | 1.156666 | 0.340489 | 0.998305 | 0.950152 | 0.71624 | 0.934672 | 0.342815 | 1.172887 | 1.042318 | 1.275073 | 0.373822 | 0.938009 | 0.899313 | 0.509279 | 1.020897 | 1.124946 | 1.303755 | 0.98758 | 1.050975 | 1.01624 | 1.053013 | 0.854709 | 1.007623 | 0.541313 |
7 | 0.668893 | 1.142516 | 1.082317 | 1.001508 | 1.257965 | 1.156666 | 0 | 1.028814 | 1.508507 | 1.13499 | 1.385958 | 1.335947 | 1.156748 | 0.732037 | 0.262941 | 0.697594 | 1.026523 | 1.311118 | 1.157045 | 1.140503 | 0.598434 | 0.949951 | 1.353931 | 1.319626 | 1.252699 | 1.112118 | 1.021524 | 1.192132 | 1.127319 | 1.120513 |
8 | 1.06268 | 0.866625 | 0.265972 | 0.315122 | 0.382349 | 0.340489 | 1.028814 | 0 | 0.760827 | 0.775155 | 0.513933 | 0.920736 | 0.505828 | 0.960048 | 0.957057 | 1.084179 | 0.235999 | 0.845577 | 0.906334 | 0.473764 | 0.857603 | 0.924401 | 1.276137 | 1.085939 | 1.04134 | 0.9678 | 1.015197 | 0.893504 | 0.857081 | 0.617177 |
9 | 1.299697 | 1.259231 | 0.902611 | 0.701318 | 0.739377 | 0.998305 | 1.508507 | 0.760827 | 0 | 1.002217 | 0.339485 | 1.182081 | 1.069632 | 1.055532 | 1.453775 | 1.23868 | 0.860223 | 1.011237 | 1.328424 | 0.843694 | 1.123626 | 0.943992 | 1.405014 | 1.359142 | 1.152356 | 1.190968 | 1.35203 | 1.260603 | 1.044504 | 1.071701 |
10 | 1.18704 | 0.504748 | 0.892251 | 0.783516 | 0.861793 | 0.950152 | 1.13499 | 0.775155 | 1.002217 | 0 | 0.914041 | 0.610368 | 0.954605 | 0.981486 | 1.031455 | 1.159442 | 0.808123 | 0.434747 | 0.638751 | 0.751498 | 0.959398 | 0.99611 | 1.61402 | 1.509281 | 1.437025 | 1.37683 | 1.42397 | 1.347339 | 1.256759 | 1.175614 |
11 | 1.23804 | 1.125121 | 0.599579 | 0.549547 | 0.449417 | 0.71624 | 1.385958 | 0.513933 | 0.339485 | 0.914041 | 0 | 1.049704 | 0.829555 | 1.026651 | 1.315511 | 1.208061 | 0.598341 | 0.899271 | 1.197162 | 0.680137 | 1.0304 | 0.897864 | 1.279529 | 1.191058 | 1.063456 | 1.068774 | 1.214896 | 1.029793 | 0.897967 | 0.862298 |
12 | 1.299576 | 0.371013 | 1.002578 | 0.88278 | 0.827537 | 0.934672 | 1.335947 | 0.920736 | 1.182081 | 0.610368 | 1.049704 | 0 | 0.75917 | 1.196501 | 1.133237 | 1.252352 | 0.943026 | 0.28334 | 0.425452 | 0.598741 | 1.046518 | 1.074504 | 1.753761 | 1.472285 | 1.430065 | 1.56314 | 1.647388 | 1.488928 | 1.568603 | 1.279363 |
13 | 1.196235 | 0.760708 | 0.560964 | 0.540252 | 0.500326 | 0.342815 | 1.156748 | 0.505828 | 1.069632 | 0.954605 | 0.829555 | 0.75917 | 0 | 1.171333 | 0.991084 | 1.185791 | 0.522434 | 0.829978 | 0.736452 | 0.340791 | 0.955333 | 1.066353 | 1.473265 | 1.093417 | 1.111248 | 1.208948 | 1.260726 | 1.085965 | 1.244388 | 0.77904 |
14 | 0.618855 | 1.141332 | 1.050841 | 0.859854 | 1.077903 | 1.172887 | 0.732037 | 0.960048 | 1.055532 | 0.981486 | 1.026651 | 1.196501 | 1.171333 | 0 | 0.70495 | 0.406511 | 0.969142 | 1.075495 | 1.216231 | 1.019441 | 0.331276 | 0.382049 | 1.093773 | 1.196759 | 1.001423 | 0.960882 | 1.036451 | 1.091829 | 0.913741 | 1.096156 |
15 | 0.665795 | 0.955521 | 1.017548 | 0.918177 | 1.143161 | 1.042318 | 0.262941 | 0.957057 | 1.453775 | 1.031455 | 1.315511 | 1.133237 | 0.991084 | 0.70495 | 0 | 0.657037 | 0.948424 | 1.140946 | 0.961707 | 0.97906 | 0.495037 | 0.867444 | 1.337258 | 1.217928 | 1.168593 | 1.109707 | 1.056164 | 1.155213 | 1.174098 | 1.056249 |
16 | 0.54746 | 1.209867 | 1.165269 | 0.910809 | 1.185613 | 1.275073 | 0.697594 | 1.084179 | 1.23868 | 1.159442 | 1.208061 | 1.252352 | 1.185791 | 0.406511 | 0.657037 | 0 | 1.092387 | 1.185866 | 1.250054 | 1.060132 | 0.301763 | 0.44519 | 1.358727 | 1.358062 | 1.141172 | 1.212851 | 1.265792 | 1.334497 | 1.202692 | 1.282908 |
17 | 1.158025 | 0.862577 | 0.287241 | 0.36856 | 0.493578 | 0.373822 | 1.026523 | 0.235999 | 0.860223 | 0.808123 | 0.598341 | 0.943026 | 0.522434 | 0.969142 | 0.948424 | 1.092387 | 0 | 0.85179 | 0.928948 | 0.567834 | 0.857454 | 0.931173 | 1.30512 | 1.149676 | 1.146966 | 1.026035 | 1.054834 | 0.846739 | 0.840021 | 0.657462 |
18 | 1.278158 | 0.444649 | 0.931873 | 0.797291 | 0.779071 | 0.938009 | 1.311118 | 0.845577 | 1.011237 | 0.434747 | 0.899271 | 0.28334 | 0.829978 | 1.075495 | 1.140946 | 1.185866 | 0.85179 | 0 | 0.576478 | 0.629673 | 0.985171 | 0.955268 | 1.684389 | 1.496597 | 1.429538 | 1.501044 | 1.593105 | 1.417819 | 1.42315 | 1.252607 |
19 | 1.27371 | 0.188373 | 1.030943 | 0.879939 | 0.964978 | 0.899313 | 1.157045 | 0.906334 | 1.328424 | 0.638751 | 1.197162 | 0.425452 | 0.736452 | 1.216231 | 0.961707 | 1.250054 | 0.928948 | 0.576478 | 0 | 0.632796 | 1.044793 | 1.203043 | 1.78631 | 1.46886 | 1.455258 | 1.520597 | 1.543672 | 1.480947 | 1.570341 | 1.200231 |
20 | 1.075207 | 0.643415 | 0.631289 | 0.41629 | 0.449985 | 0.509279 | 1.140503 | 0.473764 | 0.843694 | 0.751498 | 0.680137 | 0.598741 | 0.340791 | 1.019441 | 0.97906 | 1.060132 | 0.567834 | 0.629673 | 0.632796 | 0 | 0.85269 | 0.909121 | 1.461698 | 1.131394 | 1.050337 | 1.189165 | 1.276042 | 1.17081 | 1.227809 | 0.845935 |
21 | 0.531415 | 0.995657 | 0.927306 | 0.739009 | 0.95813 | 1.020897 | 0.598434 | 0.857603 | 1.123626 | 0.959398 | 1.0304 | 1.046518 | 0.955333 | 0.331276 | 0.495037 | 0.301763 | 0.857454 | 0.985171 | 1.044793 | 0.85269 | 0 | 0.380339 | 1.188123 | 1.146613 | 0.988463 | 1.02683 | 1.081507 | 1.092002 | 1.024229 | 1.04195 |
22 | 0.701549 | 1.125258 | 0.978781 | 0.781714 | 0.925659 | 1.124946 | 0.949951 | 0.924401 | 0.943992 | 0.99611 | 0.897864 | 1.074504 | 1.066353 | 0.382049 | 0.867444 | 0.44519 | 0.931173 | 0.955268 | 1.203043 | 0.909121 | 0.380339 | 0 | 1.211884 | 1.241849 | 1.029062 | 1.124693 | 1.247636 | 1.175577 | 1.043333 | 1.173268 |
23 | 1.223166 | 1.74038 | 1.272868 | 1.400413 | 1.307955 | 1.303755 | 1.353931 | 1.276137 | 1.405014 | 1.61402 | 1.279529 | 1.753761 | 1.473265 | 1.093773 | 1.337258 | 1.358727 | 1.30512 | 1.684389 | 1.78631 | 1.461698 | 1.188123 | 1.211884 | 0 | 0.638956 | 0.69 | 0.453356 | 0.624937 | 0.633601 | 0.761861 | 0.903073 |
24 | 1.182887 | 1.479599 | 1.113743 | 1.215507 | 1.069247 | 0.98758 | 1.319626 | 1.085939 | 1.359142 | 1.509281 | 1.191058 | 1.472285 | 1.093417 | 1.196759 | 1.217928 | 1.358062 | 1.149676 | 1.496597 | 1.46886 | 1.131394 | 1.146613 | 1.241849 | 0.638956 | 0 | 0.404254 | 0.567051 | 0.717915 | 0.708742 | 1.038811 | 0.646713 |
25 | 0.956577 | 1.465247 | 1.116642 | 1.097629 | 1.017394 | 1.050975 | 1.252699 | 1.04134 | 1.152356 | 1.437025 | 1.063456 | 1.430065 | 1.111248 | 1.001423 | 1.168593 | 1.141172 | 1.146966 | 1.429538 | 1.455258 | 1.050337 | 0.988463 | 1.029062 | 0.69 | 0.404254 | 0 | 0.576801 | 0.784341 | 0.890079 | 1.022617 | 0.762774 |
26 | 1.063487 | 1.493416 | 1.023618 | 1.10128 | 1.095686 | 1.01624 | 1.112118 | 0.9678 | 1.190968 | 1.37683 | 1.068774 | 1.56314 | 1.208948 | 0.960882 | 1.109707 | 1.212851 | 1.026035 | 1.501044 | 1.520597 | 1.189165 | 1.02683 | 1.124693 | 0.453356 | 0.567051 | 0.576801 | 0 | 0.268779 | 0.530916 | 0.590373 | 0.554163 |
27 | 1.110752 | 1.522715 | 1.06467 | 1.161674 | 1.20998 | 1.053013 | 1.021524 | 1.015197 | 1.35203 | 1.42397 | 1.214896 | 1.647388 | 1.260726 | 1.036451 | 1.056164 | 1.265792 | 1.054834 | 1.593105 | 1.543672 | 1.276042 | 1.081507 | 1.247636 | 0.624937 | 0.717915 | 0.784341 | 0.268779 | 0 | 0.567637 | 0.625095 | 0.582305 |
28 | 1.290979 | 1.422669 | 0.846439 | 1.075774 | 0.996923 | 0.854709 | 1.192132 | 0.893504 | 1.260603 | 1.347339 | 1.029793 | 1.488928 | 1.085965 | 1.091829 | 1.155213 | 1.334497 | 0.846739 | 1.417819 | 1.480947 | 1.17081 | 1.092002 | 1.175577 | 0.633601 | 0.708742 | 0.890079 | 0.530916 | 0.567637 | 0 | 0.531686 | 0.517798 |
29 | 1.187189 | 1.487738 | 0.851927 | 0.989285 | 1.034318 | 1.007623 | 1.127319 | 0.857081 | 1.044504 | 1.256759 | 0.897967 | 1.568603 | 1.244388 | 0.913741 | 1.174098 | 1.202692 | 0.840021 | 1.42315 | 1.570341 | 1.227809 | 1.024229 | 1.043333 | 0.761861 | 1.038811 | 1.022617 | 0.590373 | 0.625095 | 0.531686 | 0 | 0.732423 |
30 | 1.19892 | 1.189664 | 0.689503 | 0.808469 | 0.785933 | 0.541313 | 1.120513 | 0.617177 | 1.071701 | 1.175614 | 0.862298 | 1.279363 | 0.77904 | 1.096156 | 1.056249 | 1.282908 | 0.657462 | 1.252607 | 1.200231 | 0.845935 | 1.04195 | 1.173268 | 0.903073 | 0.646713 | 0.762774 | 0.554163 | 0.582305 | 0.517798 | 0.732423 | 0 |
What to do next with this kind of information depends on what clustering algorithm you choose. To see a description of these go to the next section: Forming Clusters.