In this paper, we propose a method for determining the abnormal web pages using a Deep support vector data description (SVDD) and HDBSCAN (Hierarchical Density-based Spatial Clustering of Applications with Noise). Deep SVDD replaces the kernel of the SVDD with a neural network and minimizes hyperspheres containing normal data. The proposed method applies HDBSCAN, which is density-based clustering method, to remove abnormal data from the training data before using Deep SVDD. Through experiments on a web page dataset, we showed that HDBSCAN can remove anomalous data and that the performance of Deep SVDD is stabilised by using the removed data as training data.