Cluster analysis of Wisconsin Breast Cancer dataset using self-organizing maps.

@article{citeulike:403894, abstract = {This work deals with multidimensional data analysis, precisely cluster analysis applied to a very well known dataset, the Wisconsin Breast Cancer dataset. After the introduction of the topics of the paper the cluster analysis concept is shortly explained and different methods of cluster analysis are compared. Further, the Kohonen model of self-organizing maps is briefly described together with an example and with explanations of how the cluster analysis can be performed using the maps. After describing the data set and the methodology used for the analysis we present the findings using textual as well as visual descriptions and conclude that the approach is a useful complement for assessing multidimensional data and that this dataset has been overused for automated decision benchmarking purposes, without a thorough analysis of the data it contains.}, address = {School of Health Information Science, University of Victoria, V8W 3P5 Victoria, BC, Canada.}, author = {Pantazi, S. and Kagolovsky, Y. and Moehr, J. R. }, citeulike-article-id = {403894}, issn = {0926-9630}, journal = {Stud Health Technol Inform}, keywords = {map, organizing, self}, pages = {431--436}, posted-at = {2005-11-22 02:50:13}, priority = {3}, title = {Cluster analysis of Wisconsin Breast Cancer dataset using self-organizing maps.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/15460731}, volume = {90}, year = {2002} }

TY - JOUR ID - citeulike:403894 L3 - citeulike-article-id:403894 TI - Cluster analysis of Wisconsin Breast Cancer dataset using self-organizing maps. JF - Stud Health Technol Inform VL - 90 SP - 431 EP - 436 AD - School of Health Information Science, University of Victoria, V8W 3P5 Victoria, BC, Canada. SN - 0926-9630 N2 - This work deals with multidimensional data analysis, precisely cluster analysis applied to a very well known dataset, the Wisconsin Breast Cancer dataset. After the introduction of the topics of the paper the cluster analysis concept is shortly explained and different methods of cluster analysis are compared. Further, the Kohonen model of self-organizing maps is briefly described together with an example and with explanations of how the cluster analysis can be performed using the maps. After describing the data set and the methodology used for the analysis we present the findings using textual as well as visual descriptions and conclude that the approach is a useful complement for assessing multidimensional data and that this dataset has been overused for automated decision benchmarking purposes, without a thorough analysis of the data it contains. KW - map KW - organizing KW - self AU - Pantazi, S AU - Kagolovsky, Y AU - Moehr, JR PY - 2002/// UR - http://view.ncbi.nlm.nih.gov/pubmed/15460731 ER -