Towards region discovery in spatial datasets

This paper presents a novel region discovery framework geared towards finding scientifically interesting places in spatial datasets. We view region discovery as a clustering problem in which an externally given fitness function has to be maximized. The framework adapts four representative clustering algorithms, exemplifying prototype-based, grid-based, density-based, and agglomerative clustering algorithms, and then we systematically evaluated the four algorithms in a real-world case study. The task is to find feature-based hotspots where extreme densities of deep ice and shallow ice co-locate on Mars. The results reveal that the density-based algorithm outperforms other algorithms inasmuch as it discovers more regions with higher interestingness, the grid-based algorithm can provide acceptable solutions quickly, while the agglomerative clustering algorithm performs best to identify larger regions of arbitrary shape. Moreover, the results indicate that there are only a few regions on Mars where shallow and deep ground ice co-locate, suggesting that they have been deposited at different geological times.