Grid-based clustering algorithm for multi-density

This paper presents a grid-based clustering algorithm for multi-density (GDD). The GDD is a kind of the multi-stage clustering that integrates grid-based clustering, the technique of density threshold descending and border points extraction. Scanning the dataset only once, the GDD can discover clusters of arbitrary shapes. The experiment results show that it can discover outliers or noises effectively and get good cluster quality for multi-data sets.