High-dimensional data structure analysis using Self-Organising Maps

Author

Hodych, Oles ; Nikolski, Iouri ; Pasichnyk, Volodymyr ; Shcherbyna, Yuri

fYear

2007

fDate

19-24 Feb. 2007

Firstpage

218

Lastpage

221

Abstract

In this article the authors discuss several approaches to high dimensional data structure analysis using self-organising maps. The described approaches utilise graphical images for the purpose of data structure interpretation. The evaluation of the discussed techniques has been performed using the real medical data from cardiology. The research, results of which are outlined in this paper, is a continuation of the earlier work related to the analysis of the same medical data.

Keywords

cardiology; data structures; data visualisation; image classification; medical image processing; neural net architecture; self-organising feature maps; artificial neural networks; cardiology medical data; data classification; data clustering; data structure interpretation; data visualisation; graphical images; high dimensional data structure analysis; self-organising maps; Biomedical imaging; Cardiology; Data analysis; Data structures; Decision making; Information systems; Lattices; Medical diagnostic imaging; Neural networks; Neurons; artificial neural networks; classification; clustering; data visualisation; diagnostics;

fLanguage

English

Publisher

ieee

Conference_Titel

CAD Systems in Microelectronics, 2007. CADSM '07. 9th International Conference - The Experience of Designing and Applications of

Conference_Location

Lviv-Polyana

Print_ISBN

966-533-587-0

Type

conf

DOI

10.1109/CADSM.2007.4297529

Filename

4297529