It is a popular and classical problem to detect a hotspot cluster from a statistical data which is partitioned by geographical regions such as prefectures or cities. Spatial scan statistic is a standard measure of likelihood ratio which has been widely used for testing hotspot clusters. In this work, we propose a very fast algorithm to enumerate all combinatorial regions which are more significant than a given threshold value. Our algorithm features the fast exploration by pruning the search space based on the partial monotonicity of the spatial scan statistic. Experimental results for a nation-wide 47 prefectures dataset show that our method generates the highest-ranked hotspot cluster in a time a million or more times faster than the previous naive search method. Our method works practically for a dataset with several hundreds of regions, and it will drastically accelerate hotspot analysis in various fields.