SUMMARY
With the rapid explosion of information based on location, spatial clustering plays an increasingly significant role in this day and age as an important technique in geographical data analysis. Most existing spatial clustering algorithms are limited by complicated spatial patterns, which have difficulty in discovering clusters with arbitrary shapes and uneven density. In order to overcome such limitations, we propose a novel clustering method called Spatial Clustering with Multiple Density-Ordered Trees (SCMDOT). Motivated by the idea of the Density-Ordered Tree (DOT), we firstly represent the original dataset by the means of constructing Multiple Density-Ordered Trees (MDOT). In the constructing process, we impose additional constraints to control the growth of each Density-Ordered Tree, ensuring that they all have high spatial similarity. Furthermore, a series of MDOT can be successively generated from regions of sparse areas to the dense areas, where each Density-Ordered Tree, also treated as a sub-tree, represents a cluster. In the merging process, the final clusters are obtained by repeatedly merging a suitable pair of clusters until they satisfy the expected clustering result. In addition, a heuristic strategy is applied during the process of our algorithm for suitability for special applications. The experiments on synthetic and real-world spatial databases are utilised to demonstrate the performance of our proposed method.