Self-organizing trees for visualizing protein dataset

Author

Nhat-Quang Doan ; Azzag, Hanane ; Lebbah, Mustapha ; Santini, G.

fYear

2013

fDate

4-9 Aug. 2013

Firstpage

1

Lastpage

8

Abstract

Clustering and visualizing multidimensional or structured data are important tasks for data analysis, especially in bioinformatics. Self-organizing models are often used to address both of these issues. In this paper we introduce a hierarchical and topological visualization technique called Self-organizing Trees (SoT) which is able to represent data in hierarchical and topological structure. The experiment is conducted on a real-world protein data set.

Keywords

bioinformatics; data visualisation; pattern clustering; proteins; self-organising feature maps; trees (mathematics); SoT; bioinformatics; clustering; data analysis; hierarchical structure; hierarchical visualization technique; multidimensional data; protein dataset; self-organizing trees; structured data; topological structure; topological visualization technique; Data visualization; Laplace equations; Proteins; Prototypes; Three-dimensional displays; Topology; Vectors; Clustering; hierarchical trees; protein data; protein domains; self-organizing map; visualization;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), The 2013 International Joint Conference on

Conference_Location

Dallas, TX

ISSN

2161-4393

Print_ISBN

978-1-4673-6128-6

Type

conf

DOI

10.1109/IJCNN.2013.6707001

Filename

6707001