PPoSOM: A new variant of PolSOM by using probabilistic assignment for multidimensional data visualization

Abstract

A new Self-Organizing Map algorithm, called the probabilistic polar self-organizing map (PPoSOM), is proposed. PPoSOM is a new variant of PolSOM, which is constructed on 2-D polar coordinates. Two variables: radius and angle are used to reflect the data characteristics. PPoSOM, developed to enhance the visualization performance, provides more data characteristics compared with the traditional methods that use Euclidian distance as the only variable. The weight-updating rule of PPoSOM is associated with a cost function. Instead of using the hard assignment, PPoSOM employs the soft assignment that the assignment of data to neuron is based on a probabilistic function. The obtained results are compared with the conventional SOM and ViSOM. The presented results show that the proposed PPoSOM is an effective method for multidimensional data visualization. In addition, the quality measurement of mapping, synthetical cluster density (SCD) is applied and it shows PPoSOM exhibits an improved result compared with PolSOM.