کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
9653604 | 679206 | 2005 | 22 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Visualizing asymmetric proximities with SOM and MDS models
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Visualizing asymmetric proximities with SOM and MDS models Visualizing asymmetric proximities with SOM and MDS models](/preview/png/9653604.png)
چکیده انگلیسی
Multidimensional scaling (MDS) and self organizing maps (SOM) algorithms are useful to visualize object relationships in a data set. These algorithms rely on the use of symmetric distances or similarity measures; for instance the Euclidean distance. There are a number of relevant applications, such as text mining and DNA microarray processing for which it is worth considering non symmetric similarity measures, that allow us to properly represent hierarchical relationships. In this paper we present asymmetric versions of SOM and MDS algorithms able to deal with asymmetric proximity matrices. We also compare these approaches to the corresponding symmetric versions. Experimental work on text databases and gene expression data sets show that the asymmetric proposed algorithms outperform their symmetric counterparts.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 63, January 2005, Pages 171-192
Journal: Neurocomputing - Volume 63, January 2005, Pages 171-192
نویسندگان
Manuel MartÃn-Merino, Alberto Muñoz,