Data Clustering and Topology Preservation Using 3D Visualization of Self Organizing Maps
The Self Organizing Maps (SOM) is regarded as an excellent computational tool that can be used in data mining and data exploration processes. The SOM usually create a set of prototype vectors representing the data set and carries out a topology preserving projection from high-dimensional input space onto a low-dimensional grid such as Two-Dimensional (2D) regular grid or 2D map. The 2D-SOM technique can be effectively utilized to visualize and explore the properties of the data. This technique has been applied in numerous application areas such as in pattern recognition, robotics, bioinformatics and also life sciences including clustering complex gene expression patterns.