Title :
DS_CABOSFV clustering algorithm for high dimensional data stream
Author_Institution :
Dongling School of Economics Management, University of Science and Technology Beijing, China
Abstract :
Data stream clustering has become a hot research issue. The high-dimensional data stream clustering is a difficult problem for the data stream mining because the large volumes of data arriving in a stream make most traditional algorithms too inefficient. In this paper, DS_CABOSFV, a high-dimensional data stream clustering algorithm based on CABOSFV algorithm is presented. Our empirical tests show that DS_CABOSFV has low computational complexity and good efficiency for high-dimensional data stream clustering.
Keywords :
clustering; high dimensionality; stream data mining;
Conference_Titel :
Awareness Science and Technology (iCAST), 2012 4th International Conference on
Conference_Location :
Seoul, Korea (South)
Print_ISBN :
978-1-4673-2111-2
Electronic_ISBN :
978-1-4673-2110-5
DOI :
10.1109/iCAwST.2012.6469582