%0 Journal Article %1 kruskal1964 %A Kruskal, J.B. %D 1964 %I Springer %J Psychometrika %K clustering dataanalysis dimensionalityreduction method %N 1 %P 1--27 %T Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis %V 29 %X Multidimensional scaling is the problem of representing n objects: geometrically by n points, so that the interpoint distances correspond in some sense to experimental dissimilarities between objects. In just what sense distances and dissimilarities should correspond has been left rather vague in most approaches, thus leaving these approaches logically incomplete. Our fundamental hypothesis is that dissimilarities and distances are mono- tonically related. We define a quantitative, intuitively satisfying measure of goodness of fit to this hypothesis. Our technique of multidimensional scaling is to compute that confi~xlration of points which optimizes the goodness of fit. A practical computer program for doing the calculations is described in a compamon paper.

@article{kruskal1964, abstract = { Multidimensional scaling is the problem of representing n objects: geometrically by n points, so that the interpoint distances correspond in some sense to experimental dissimilarities between objects. In just what sense distances and dissimilarities should correspond has been left rather vague in most approaches, thus leaving these approaches logically incomplete. Our fundamental hypothesis is that dissimilarities and distances are mono- tonically related. We define a quantitative, intuitively satisfying measure of goodness of fit to this hypothesis. Our technique of multidimensional scaling is to compute that confi~xlration of points which optimizes the goodness of fit. A practical computer program for doing the calculations is described in a compamon paper. }, added-at = {2007-12-21T13:32:52.000+0100}, author = {Kruskal, J.B.}, biburl = {https://www.bibsonomy.org/bibtex/2cff2b1616b46e3c2a01544135da475a8/tmalsburg}, interhash = {c03a4ba65e0b871bb3be96f5d7356be4}, intrahash = {cff2b1616b46e3c2a01544135da475a8}, journal = {Psychometrika}, keywords = {clustering dataanalysis dimensionalityreduction method}, number = 1, pages = {1--27}, publisher = {Springer}, timestamp = {2007-12-21T13:35:57.000+0100}, title = {{Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis}}, volume = 29, year = 1964 }