Title :
Unsupervised Clustering In Streaming Data
Author :
Tasoulis, Dimitris K. ; Adams, Niall M. ; Hand, David J.
Author_Institution :
Inst. for Math. Sci., Imperial Coll., London
Abstract :
Tools for automatically clustering streaming data are becoming increasingly important as data acquisition technology continues to advance. In this paper we present an extension of conventional kernel density clustering to a spatio-temporal setting, and also develop a novel algorithmic scheme for clustering data streams. Experimental results demonstrate both the high efficiency and other benefits of this new approach
Keywords :
data mining; pattern clustering; conventional kernel density clustering; data clustering; streaming data; unsupervised clustering; Clustering algorithms; Clustering methods; Data acquisition; Data mining; Data models; Databases; Educational institutions; Kernel; Partitioning algorithms; Scalability;
Conference_Titel :
Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
0-7695-2702-7
DOI :
10.1109/ICDMW.2006.165
