Space Breakdown Method A new approach for densitybased 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 xe2x80x93 not all data points need to be clustered, and the data space has a relatively low dimensionality xe2x80x93 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 wellknown clustering algorithms.

Segmentation of CA3 Hippocampal Region of Rat Brain Cells Images Based on Bioinspired Clustering Technique In the area of clustering, the most common issue of obtaining the optimum number value for the clusters is still an open challenge for different application areas. It is very hard to get the optimal number of clusters because of the lack of prior knowledge. This happens due to having various dimensions of data, clusters having a wide range of shape, size u0026 density, and overlapping exists among groups. Many approaches have been proposed by various researchers which include bioinspired techniques like genetic algorithm, particle swarm optimization, invasive weed optimization, cat swarm optimization, ant colony optimization, etc., for addressing these issues. Also, various combinations of the hybridization of these techniques have been practices by the researchers. The superiority of evolutionary techniques over the hard clustering techniques such as kmeans clustering becomes popular in clustering area. Inspired by this, a novel rat brain cell segmentation approach is proposed using the latest bioinspired clustering technique known as xe2x80x9cTeacherLearner Based Optimizationxe2x80x9d. In contrast to most of the wellknown clustering techniques, TLBO doesnu0027t require any parameter tuning and is less complex. The proposed approach is validated using NISSL stained rat brain cell dataset. In experimental evaluation performance, comparisons are made, based on quantitative results as well as qualitative results. The overall result analysis shows that the proposed approach is much more capable in segmenting the cells in comparison to the other wellknown clustering techniques.