TY - GEN
T1 - Clustering high dimensional data streams at multiple time granularities
AU - Yan, Xiao Long
AU - Shen, Hong
PY - 2008
Y1 - 2008
N2 - In this paper, we extend our DGStream (Dense Grid-tree based data stream clustering) method which is developed recently [14] and propose a new method DGMStream (Dense Grid-tree based multiple time granularity adaptable data stream clustering) to cluster dynamic data streams. In DGMStream, we incorporate the technique of tilted time window in DGStream to find clusters for data streams over multiple time granularities. Implementation results show that this method has a better cluster purity and scalability than other methods.
AB - In this paper, we extend our DGStream (Dense Grid-tree based data stream clustering) method which is developed recently [14] and propose a new method DGMStream (Dense Grid-tree based multiple time granularity adaptable data stream clustering) to cluster dynamic data streams. In DGMStream, we incorporate the technique of tilted time window in DGStream to find clusters for data streams over multiple time granularities. Implementation results show that this method has a better cluster purity and scalability than other methods.
UR - http://www.scopus.com/inward/record.url?scp=51949114308&partnerID=8YFLogxK
U2 - 10.1109/ICIEA.2008.4582959
DO - 10.1109/ICIEA.2008.4582959
M3 - Conference contribution
AN - SCOPUS:51949114308
SN - 9781424417186
T3 - 2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008
SP - 2458
EP - 2463
BT - 2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008
T2 - 2008 3rd IEEE Conference on Industrial Electronics and Applications, ICIEA 2008
Y2 - 3 June 2008 through 5 June 2008
ER -