Title :
Extension of iVAT to asymmetric matrices
Author :
Havens, Timothy C. ; Bezdek, James C. ; Leckie, Christopher ; Palaniswami, Marimuthu
Author_Institution :
Dept. of Electr. & Comput. Eng., Michigan Technol. Univ., Houghton, MI, USA
Abstract :
The iVAT algorithm reorders (symmetric) dissimilarity data so that an image of the data may reveal cluster substructure. This paper extends the method so that it can handle asymmetric dissimilarity data. The extension is based on replacing the asymmetric input data with its unique least-squared error approximation by a symmetric matrix. Examples are given to illustrate the new method, called asymmetric iVAT (asiVAT).
Keywords :
data handling; least squares approximations; matrix algebra; asiVAT; asymmetric dissimilarity data handling; asymmetric iVAT; asymmetric matrices; cluster substructure; iVAT algorithm; least-squared error approximation; symmetric data; symmetric matrix; Clustering algorithms; Contamination; Iris; Symmetric matrices; Tin; Vectors; Visualization; VAT; asymmetric matrices; iVAT; reordered dissimilarity images; visualization;
Conference_Titel :
Fuzzy Systems (FUZZ), 2013 IEEE International Conference on
Conference_Location :
Hyderabad
Print_ISBN :
978-1-4799-0020-6
DOI :
10.1109/FUZZ-IEEE.2013.6622300
