The recursive scheme of clustering
This work addresses clustering challenges for researchers dealing with noisy experimental data in domains like climatology, but it appears incremental as it builds on existing methods like k-means and SOM.
The paper tackles the problem of clustering data with measurement uncertainties, particularly in geographical or climatological experiments, by proposing a recursive scheme; it shows that this approach yields more acceptable results compared to expert assessments when applied with k-means and SOM methods.
The problem of data clustering is one of the most important in data analysis. It can be problematic when dealing with experimental data characterized by measurement uncertainties and errors. Our paper proposes a recursive scheme for clustering data obtained in geographical (climatological) experiments. The discussion of results obtained by k-means and SOM methods with the developed recursive procedure is presented. We show that the clustering using the new approach gives more acceptable results when compared to experts assessments.