Title :
An incremental grid clustering algorithm based on density-dimension-tree
Author :
Jiaolong Huang ; Xiaolong Zhang
Author_Institution :
Sch. of Comput. Sci. & Technol., Wuhan Univ. of Sci. & Technol., Wuhan, China
Abstract :
This paper proposes an approach to improve the existing grid-based clustering algorithms with a further grid partition strategy and an incremental clustering function. This new algorithm IGDDT is based on density-dimension tree, which has the ability to reuse the previous clustering results, and obtain the better clusters by further dividing the grid cell in the clustering process. The experimental results on both artificial and real datasets demonstrate that IGDDT is able to discover arbitrary shape of clusters, better performance than the previous clustering algorithms on both clustering accuracy and clustering efficiency.
Keywords :
data mining; grid computing; pattern clustering; trees (mathematics); IGDDT; arbitrary shape; artificial datasets; clustering accuracy; clustering efficiency; data mining; density-dimension-tree; grid cell; grid partition strategy; incremental clustering function; real datasets; Abstracts; Switches; Data stream; Density-dimension tree; Grid; Incremental clustering;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2013 International Conference on
Conference_Location :
Tianjin
DOI :
10.1109/ICMLC.2013.6890494