Combined Method for Effective Clustering based on Parallel SOM and Spectral Clustering.

The paper is oriented to the problem of clustering for large datasets with high-dimensions. We propose a two-phase combined method with regard to high dimensions and exploiting the standard clustering algorithm. The first step of the method is based on the learn- ing phase using artificial neural network, especially Self organizing map, which we find as a suitable method for the reduction of the problem complexity. Due to the fact, that the learning phase of artificial neural networks can be time-consuming operation (especially for large high- dimensional datasets), we decided to accelerate this phase using paral- lelization to improve the computational eciency. The second phase of the proposed method is oriented to clustering. Because the visualization provided by Self organizing maps is depending on the map dimension, and is not as clear and comprehensible in the cases of clustering applica- tions, we decided to use spectral clustering algorithm to obtain sucient clusters. According to our results, the proposed combined method is suciently rapid and quite accurate.