Algorithm for Spatial Clustering with Obstacles

TL;DR

This paper introduces an efficient spatial clustering algorithm that accounts for obstacles by dividing the area into cells, labeling them based on density and obstruction, and then identifying connected dense regions as clusters.

Contribution

The paper presents a novel obstacle-aware clustering algorithm that effectively handles spatial obstacles by integrating obstacle information into the clustering process.

Findings

Successfully identifies clusters in obstacle-rich environments

Efficiently processes large spatial datasets

Accurately finds centers of dense regions

Abstract

In this paper, we propose an efficient clustering technique to solve the problem of clustering in the presence of obstacles. The proposed algorithm divides the spatial area into rectangular cells. Each cell is associated with statistical information that enables us to label the cell as dense or non-dense. We also label each cell as obstructed (i.e. intersects any obstacle) or non-obstructed. Then the algorithm finds the regions (clusters) of connected, dense, non-obstructed cells. Finally, the algorithm finds a center for each such region and returns those centers as centers of the relatively dense regions (clusters) in the spatial area.