Spatial point data is an important data source that represents the locations of spatial events (such as crime, disease cases, and earthquakes). Detecting clusters I n spatial point data plays a key role in exploratory spatial data analysis. Although much attention has been paid to the clustering of spatial points, how to automatically and efficiently discover the statistically significant clusters with irregular shapes is still a challenging work. On that account, an automatic method to detect the statistically significant high-density clusters in spatial point data with noise is proposed in this paper. First, the Voronoi diagram of the spatial points is constructed, and the densities of spatial points are defined by the areas of the Voronoi cells. Then, high-density points are automatically detected using spatial hotspot statistics analysis, and a density-based clustering strategy is further adapted to combine the neighboring high-density points into candidate clusters. Finally, a statistical significance test is proposed to evaluate the significance of the candidate clusters under the spatially homogeneous or heterogeneous distribution assumption. We tested the proposed method with the simulated data sets and the real-world taxi trajectory data for detecting the pick-up hotspot regions in Wuhan, China. Results show that the proposed method can successfully find arbitrarily shaped significant clusters that existing state-of-the-art clustering algorithms may fail to find in spatial point data with noise.