DS_CABOSFV Clustering Algorithm for High Dimensional Data Stream

被引：0

作者：

Pan, Jing ^{[1
]}

机构：

[1] Univ Sci & Technol Beijing, Dongling Sch Econ Management, Beijing 100083, Peoples R China

来源：

4TH INTERNATIONAL CONFERENCE ON AWARENESS SCIENCE AND TECHNOLOGY (ICAST 2012) | 2012年

关键词：

stream data mining; clustering; high dimensionality;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页码：16 / 19

页数：4

共 11 条

[1]

Aggarwal C., 2003, A Framework for Clustering Evolving Data Streams

[2]

Aggarwal C.C., 2004, Proceedings of the Thirtieth International Conference on Very Large Data Bases-Volume 30, VLDB '04

[3]

Agrawal R., 1998, Proc. of ACM SIGMOD, P94

[4]

[Anonymous], J COMPUT RES DEV S

[5]

[Anonymous], IEEE TKDE SPECIAL IS

[6]

Babu Babcock S, 2002, P 21 ACM SYMP PRINC

[7]

Guha S., 1998, SIGMOD Record, V27, P73, DOI 10.1145/276305.276312

[8]

Han J., 2012, Data Mining, P393, DOI [DOI 10.1016/B978-0-12-381479-1.00009-5, 10.1016/B978-0-12-381479-1.00009-5]

[9]

Jin Che-Qing, 2004, Journal of Software, V15, P1172

[10]

Nasraoui O, 2003, P 3 IEEE INT C DAT M, P19

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