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POClustering 2D plot in Mathematica
 

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    <pre><code>laListe={{{{10, 17}, 1}, {{33, 12}, 1}, {{32, 17}, 1}, {{9, 10},1}, {{22, 24}, 1},{{27, 6}, 2}, {{25, 13}, 2}, {{30, 9}, 2}}, {{{14, 12}, 1},{{19, 17}, 1}, {{7, 21}, 1}, {{7, 24},1}, {{27, 19}, 1}, {{12, 16}, 2}, {{13, 20}, 2}, {{20, 22}, 2}}} FrameXYs = {{4.32, 3.23}, {35.68, 26.75}} Row[Function[compNo, Graphics[{White, EdgeForm[Thick], Rectangle @@ FrameXYs, Black, Disk[Sequence @@ laListe[[compNo, #]]] &amp; /@ Range[Length@laListe[[compNo]]]}, ImageSize -&gt; 300]] /@ {1, 2}] </code></pre> <p><img src="https://i.stack.imgur.com/aimde.png" alt="enter image description here"></p> <p>I would like to find a way to cluster those disk given their proximity to each other. Does Mathematica have built in feature to do such thing ?</p> <p><strong>EDIT</strong> </p> <p>As I tried FindClusters I yet encounter several inconvenience :</p> <p>With :</p> <pre><code>list1={{{24.413, 6.5978}, {7.68887, 7.2147}, {29.357, 13.2822}, {6.22436, 9.7145}, {22.7162, 17.7198}, {13.6851, 5.7635}, {18.8062, 12.9946}, {8.04889, 16.7414}}} </code></pre> <p>Does FindClusters dislkike Decimals :</p> <pre><code>FindClusters[Flatten[list1,1]] </code></pre> <p>Out :</p> <pre><code> {{{{24.413, 6.5978}, {7.68887, 7.2147}, {29.357, 13.2822}, {6.22436,9.7145}, {22.7162, 17.7198}, {13.6851, 5.7635}, {18.8062,12.9946}, {8.04889, 16.7414}}}} </code></pre> <p>Whereas : </p> <pre><code> FindClusters[Flatten[Round[list1], 1]] </code></pre> <p>Out : </p> <pre><code> {{{24, 7}, {29, 13}, {23, 18}, {14, 6}, {19, 13}}, {{8, 7}, {6, 10}, {8, 17}}} </code></pre> <p>Then, to do this I had to get rid of the Disks Diameter which is important to me as visual cluster. Then I would like to capture alignment. When 5 disks are not grouped but aligned. And as I tested it on a few composition it does not find those as such.</p> <p>On thing I am trying is tho "Pointize" the disks using the following :</p> <pre><code>pointize[{{x_,y_},r_},size_:12] := Table[{x+r Cos[i ((2\[Pi])/size)], y+r Sin[i ((2\[Pi])/size)]},{i,0,size}] </code></pre> <p>I used that initially to compute ConvexHullArea of those disks. I feel it could help my need of taking into accound the radius, but the implementation is tricky and I am not even sure if it is relevant</p> <p>Also, I hope it was only the decimals issue, but I could not use FindClusters[list] as such but had to give it the number of cluster I want FindClusters[list,3], whereas what I want is to have the same algorithm that can find different cluster number on different composition. </p> <p>Would you think of particular settings &amp;/or distance function to do so with FindClusters?</p> <p><strong>EDIT</strong></p> <p>I found something interesting thanks to previous tricks learned thanks to experts here. Just an idea, I need to fin a way quantify that and put the new image in a matrix form or so to use .</p> <pre><code>comp1 = Graphics[{White, Rectangle @@ FrameXYs, Black, Disk[Sequence @@ laListe[[1, #]]] &amp; /@ Range[Length@laListe[[1]]]}, ImageSize -&gt; 300] </code></pre> <p><img src="https://i.stack.imgur.com/NhKBl.png" alt="enter image description here"></p> <pre><code> Binarize[ImageCorrelate[comp1, GaussianMatrix[40]], .95] </code></pre> <p><img src="https://i.stack.imgur.com/j5vmJ.png" alt="enter image description here"></p>
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    1. COSee my answer here http://stackoverflow.com/questions/3165867/create-a-summary-description-of-a-schedule-given-a-list-of-shifts/3251229#3251229
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    2. COI don't see why you had to get rid of the bubble diameter - it is effectively your third dimension, isn't it? Also you don't have to specify the number of clusters to find. Your latest edit with the Gaussian blobs is cool, but I think it's a separate question.
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    3. COVerbeia, I guess I am not agile with find clusters, I thought I could not have my third dimension (radius) in it, I will try harder.
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