Fast clustering algorithm based on reference and density

Abstract

The efficiency of data mining algorithms is strongly needed with data becoming larger and larger. Density-Based clustering analysis is one kind of clustering analysis methods that can discover clusters with arbitrary shape and is insensitive to noise data. A new kind of clustering algorithm that is called CURD (clustering using references and density) is presented. The creativity of CURD is capturing the shape and extent of a cluster by references, and then analyzes the data based on the references. CURD keeps the ability of density based clustering method's good features, and it can reach high efficiency because of its linear time complexity, so it can be used in mining very large databases. Both theory 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 traditional DBSCAN algorithm based on R-tree.