A New Fast Clustering Algorithm Based on Reference and Density

Density-based clustering is a sort of clustering analysis methods, which can discover clusters with arbitrary shape and is insensitive to noise data. The efficiency of data mining algorithms is strongly needed with data becoming larger and larger. In this paper, we present a new fast clustering algorithm called CURD, which means Clustering Using References and Density. Its creativity is capturing the shape and extent of a cluster with references, and then it analyzes the data based on the references. CURD preserves the ability of density based clustering method’s good advantages, and it is much efficient because of its nearly linear time complexity, so it can be used in mining very large databases. Both our theoretic analysis and experimental results confirm that CURD can discover clusters with arbitrary shape and is insensitive to noise data. In the meanwhile, its executing efficiency is much higher than R{star}}-tree based DBSCAN algorithm.