An Improved Density-Based Clustering Algorithm
Jianguo Zheng · Microcomputer Development · 2005
Density-based clustering analysis is a kind of clustering analysis methods that can discover clusters with arbitrary shape and is insensitive to noise data.However,existing work in clustering analysis cannot deal with database with uneven distribution efficiently.In this paper,an improved algorithm is presented.It keeps the good features of density-based clustering method, and it can also do efficiently when it face with the database with uneven distribution.Furthermore,it is linear time complex, so it can be used in mining very large databases.