  Density-based clustering algorithms are widely used for discovering clusters
in pattern recognition and machine learning since they can deal with
non-hyperspherical clusters and are robustness to handle outliers. However, the
runtime of density-based algorithms are heavily dominated by finding
fixed-radius near neighbors and calculating the density, which is
time-consuming. Meanwhile, the traditional acceleration methods using indexing
technique such as KD tree is not effective in processing high-dimensional data.
In this paper, we propose a fast region query algorithm named fast principal
component analysis pruning (called FPCAP) with the help of the fast principal
component analysis technique in conjunction with geometric information provided
by principal attributes of the data, which can process high-dimensional data
and be easily applied to density-based methods to prune unnecessary distance
calculations when finding neighbors and estimating densities. As an application
in density-based clustering methods, FPCAP method was combined with the Density
Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. And
then, an improved DBSCAN (called IDBSCAN) is obtained, which preserves the
advantage of DBSCAN and meanwhile, greatly reduces the computation of redundant
distances. Experiments on seven benchmark datasets demonstrate that the
proposed algorithm improves the computational efficiency significantly.
