Evaluation of supervised and unsupervised 3D star visualisation algorithms

Authors:
Jahangheer Shaik;Mohammed Yeasin;David J. Russomanno
Affiliations:
School of Medicine, Department of Pathology and Immunology, Washington University, St. Louis, MO 63108, USA;Department of Electrical and Computer Engineering, University of Memphis, Memphis, TN 38152, USA;Purdue School of Engineering and Technology, Indiana University-Purdue University, Indianapolis, IN 46202, USA
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2013

Citing 10
Cited 0

The grand tour: a tool for viewing multidimensional data

SIAM Journal on Scientific and Statistical Computing
Visualization of a document collection: the vibe system

Information Processing and Management: an International Journal
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Semiology of graphics

Semiology of graphics
Data-adaptive test statistics for microarray data

Bioinformatics
Advanced Data Mining Technologies in Bioinformatics

Advanced Data Mining Technologies in Bioinformatics
Generalized Power Method for Sparse Principal Component Analysis

The Journal of Machine Learning Research
Ranking function based on higher order statistics (RF-HOS)for two-sample microarray experiments

ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Supervised locally linear embedding

ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Feature fusion using locally linear embedding for classification

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

The 3D Star Coordinate Projection 3DSCP visualisation algorithm has been developed to address the following key issues: 1 choosing the projection configuration autonomously; 2 preserving the data topology after projection; 3 enhancing resolution. A supervised version of 3DSCP S3DSCP is also introduced to improve the computational efficiency of 3DSCP. Comparison with other linear, non-linear and axis-based techniques is performed to illustrate the efficacy of the 3DSCP and S3DSCP methods. Empirical analyses indicate that the 3DSCP and S3DSCP algorithms find hidden patterns in data while overcoming limitations of other techniques.