Author： Shaik Jahangheer Yeasin Mohammed Russomanno David J.
Publisher： Inderscience Publishers
ISSN： 1748-5673
Source： International Journal of Data Mining and Bioinformatics, Vol.8, Iss.4, 2013-09, pp. : 443-461
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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.
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