title: Evaluation of supervised and unsupervised 3D star visualisation algorithms.
abstract: The 3D Star Coordinate Projection (3DSCP) visualisation algorithm has been developed to address the following key issues: choosing the projection configuration autonomously. preserving the data topology after projection. 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.
authors.affiliation: School of Medicine, Department of Pathology and Immunology, Washington University, St. Louis, MO 63108, USA. jshaik@path.wustl.edu
authors.forename: Jahangheer
authors.initials: J
authors.lastname: Shaik
authors.affiliation: Department of Electrical and Computer Engineering, University of Memphis, Memphis, TN 38152, USA.
authors.forename: Mohammed
authors.initials: M
authors.lastname: Yeasin
authors.affiliation: Purdue School of Engineering and Technology, Indiana University-Purdue University, Indianapolis, IN 46202, USA.
authors.forename: David J
authors.initials: DJ
authors.lastname: Russomanno
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*