Dimension Selective Self-Organizing Maps for clustering high dimensional data

High dimensional datasets usually present several dimensions which are irrelevant for certain clusters while they are relevant to other clusters. These irrelevant dimensions bring difficulties to the traditional clustering algorithms, because the high discrepancies within them can make objects appear too different to be grouped in the same cluster. Subspace clustering algorithms have been proposed to address this issue. However, the problem remains an open challenge for datasets with noise and outliers. This article presents an approach for subspace and projected clustering based on Self-Organizing Maps (SOM), that is called Dimensional Selective Self-Organizing Map. DSSOM keeps the properties of SOM and it is able to find clusters and identify their relevant dimensions, simultaneously, during the self-organizing process. The results presented by DSSOM were promising when compared with state of art subspace clustering algorithms.