Thank you for your answers, Jean-Baptiste Pressac
Traitement et analyse de bases de données Production et diffusion de corpus numériques Centre de Recherche Bretonne et Celtique Unité mixte de service (UMS) 3554 20 rue Duquesne CS 93837 29238 Brest cedex 3 tel : +33 (0)2 98 01 68 95 fax : +33 (0)2 98 01 63 93 Le 03/03/2015 11:54, federico vaggi a écrit :
Hi,a dissimilarity is simply a measure of how difference a vector A is from a vector B. A metric also has to satisfy four extra rules: http://en.wikipedia.org/wiki/Metric_(mathematics) <http://en.wikipedia.org/wiki/Metric_%28mathematics%29>Wikipedia makes it more complicated than it has to be, but basically: 1. /d/(/x/, /y/) ≥ 0 (/non-negativity <http://en.wikipedia.org/wiki/Non-negative>/, or separation axiom) 2. /d/(/x/, /y/) = 0 if and only if /x/ = /y/ (/identity of indiscernibles <http://en.wikipedia.org/wiki/Identity_of_indiscernibles>/, or coincidence axiom) 3. /d/(/x/, /y/) = /d/(/y/, /x/) (/symmetry <http://en.wikipedia.org/wiki/Symmetric_function>/) 4. /d/(/x/, /z/) ≤ /d/(/x/, /y/) + /d/(/y/, /z/) (/subadditivity <http://en.wikipedia.org/wiki/Subadditivity>/ / /triangle inequality <http://en.wikipedia.org/wiki/Triangle_inequality>/).The main one is point 4: For a lot of dissimilarities, this is not the case.For example - take a simple relationship like A likes B: Alice can like Bob, but Bob can dislike Alice It would however be meaningless to say: Alice is 2 meters from Bob, but Bob is 1 meter from Alice.A lot of clustering and scaling algorithms only work if the distance you are working with is a metric. You can sometimes force the algorithm to work with something which isn't a metric, but you will get results which can be a bit absurd, especially if your dissimilarity is particularly badly behaved.On Tue, Mar 3, 2015 at 11:42 AM, Jean-Baptiste Pressac <jean-baptiste.pres...@univ-brest.fr <mailto:jean-baptiste.pres...@univ-brest.fr>> wrote:But what could be a dissimilarity matrix in the case of events co-attended by people ? What is more, in the publication I try to reproduce, the author made a MDS from the Jaccard coefficients... What is more, my trial is inspired from a code which analyses a matrix of distances between european citie <http://baoilleach.blogspot.fr/2014/01/convert-distance-matrix-to-2d.html>s. By the way, I don't understand the meaning of the dissimilarity parameter of manifold.MDS. In which case a "dissimilarity" is euclidean ? Sorry, I am quite knew with some concepts. Jean-Baptiste Pressac Traitement et analyse de bases de données Production et diffusion de corpus numériques Centre de Recherche Bretonne et Celtique Unité mixte de service (UMS) 3554 20 rue Duquesne CS 93837 29238 Brest cedex 3 tel :+33 (0)2 98 01 68 95 <tel:%2B33%20%280%292%2098%2001%2068%2095> fax :+33 (0)2 98 01 63 93 <tel:%2B33%20%280%292%2098%2001%2063%2093> Le 03/03/2015 11:09, Joel Nothman a écrit :I think DSW_jaccard_matrix is a matrix of similarity (which is what Jaccard usually means) not of dissimilarity. Try negating it before MDS. On 3 March 2015 at 20:07, Jean-Baptiste Pressac <jean-baptiste.pres...@univ-brest.fr <mailto:jean-baptiste.pres...@univ-brest.fr>> wrote: Hello, I tried to reproduce the analysis of events co-attended by woman via manifold MDS (shared on IPython Notebook) <http://nbviewer.ipython.org/github/JBPressac/MDS-of-DGG/blob/master/Davis%20Southern%20Women%20MDS%20of%20Jaccard%20coefficient.ipynb>, but the MDS does not reflects the data. I certainly did something wrong, but I could'nt figure out how to do a proper MDS. Any clue would be appreciated. Thanks,-- Jean-Baptiste PressacTraitement et analyse de bases de données Production et diffusion de corpus numériques Centre de Recherche Bretonne et Celtique Unité mixte de service (UMS) 3554 20 rue Duquesne CS 93837 29238 Brest cedex 3 tel :+33 (0)2 98 01 68 95 <tel:%2B33%20%280%292%2098%2001%2068%2095> fax :+33 (0)2 98 01 63 93 <tel:%2B33%20%280%292%2098%2001%2063%2093> ------------------------------------------------------------------------------ Dive into the World of Parallel Programming The Go Parallel Website, sponsored by Intel and developed in partnership with Slashdot Media, is your hub for all things parallel software development, from weekly thought leadership blogs to news, videos, case studies, tutorials and more. 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