Space Breakdown Method A new approach for density-based clustering

Overlapping clusters and different density clusters are recurrent phenomena of neuronal datasets, because of how neurons fire. We propose a clustering method that is able to identify clusters of arbitrary shapes, having different densities, and potentially overlapped. The Space Breakdown Method (SBM) divides the space into chunks of equal sizes. Based on the number of points inside the chunk, cluster centers are found and expanded. Even if we consider the particularities of neuronal data in designing the algorithm – not all data points need to be clustered, and the data space has a relatively low dimensionality – it can be applied successfully to other domains involving overlapping and different density clusters as well. The experiments performed on benchmark synthetic data show that the proposed approach has similar or better results than two well-known clustering algorithms.