Title :
Subspace Clustering of High Dimensional Data Streams
Author :
Wang, Shuyun ; Fan, Yingjie ; Zhang, Chenghong ; Xu, Hexiang ; Hao, Xiulan ; Hu, Yunfa
Author_Institution :
Dept. of Comput. & Inf. Technol., Fudan Univ., Shanghai
Abstract :
In this paper, SOStream, which is a novel algorithm of clustering over high dimensional online data stream is presented, it is based on subspace.-SOStream partitions the data space into grids, and maintains a superset of all dense units in an online way. A deterministic lower and upper bound of the selectivity of each maintained units are also given. With the maintained potential dense units, SOStream is capable of discovering the clusters in different subspaces over high dimensional data stream with arbitrary shape. The experimental results on real and synthetic datasets demonstrate the effectivity of the approach.
Keywords :
data analysis; grid computing; pattern classification; SOStream algorithm; high dimensional data stream; subspace clustering; Clustering algorithms; Conference management; Grid computing; Information science; Information technology; Monitoring; Partitioning algorithms; Space technology; Technology management; Upper bound; Cluster; Data stream; high-dimensional; subspace;
Conference_Titel :
Computer and Information Science, 2008. ICIS 08. Seventh IEEE/ACIS International Conference on
Conference_Location :
Portland, OR
Print_ISBN :
978-0-7695-3131-1
DOI :
10.1109/ICIS.2008.58