An Improved Distributed Clustering Algorithm Based on Density

Abstract

The density based distributed clustering algorithm DBDC has a higher time complexity in the process of distributed clustering. We proposed an improved density based distributed clustering algorithm. This algorithm used a data grid mapping method which mapped data object to the space grid first in the local level to improve the efficiency of the implementation of the local clustering. In the global clustering level of the new algorithm, we proposed a global clustering method based on representative points intersection and uses the central point of representative point to reduce the clustering error. Experimental results showed that the proposed improved density-based distributed clustering algorithm was more accurate than DBDC.