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
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tel : +33 (0)2 98 01 68 95
fax : +33 (0)2 98 01 63 93

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 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>


    
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