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Type: Conference paper
Title: A clustering algorithm based on density-grid for stream data
Author: Zhang, D.
Tian, H.
Sang, Y.
Li, Y.
Wu, Y.
Wu, J.
Shen, H.
Citation: Proceedings, 13th International Conference on Parallel and Distributed Computing, Applications, and Technologies, PDCAT 2012, 2012 / Shen, H., Sang, Y., Li, Y., Qian, D., Zomaya, A. (ed./s), pp.398-403
Publisher: IEEE
Issue Date: 2012
ISBN: 9780769548791
Conference Name: 13th International Conference on Parallel and Distributed Computing, Applications, and Technologies (PDCAT 2012) (14 Dec 2012 - 16 Dec 2012 : Beijing, China)
Editor: Shen, H.
Sang, Y.
Li, Y.
Qian, D.
Zomaya, A.
Statement of
Dandan Zhang, Hui Tian, Yingpeng Sang, Yidong Li, Yanbo Wu, Jun Wu, Hong Shen
Abstract: Many real applications, such as network traffic monitoring, intrusion detection, satellite remote sensing, and electronic business, generate data in the form of a stream arriving continuously at high speed. Clustering is an important data analysis tool for knowledge discovery. Compared with traditional clustering algorithms, clustering stream data is an important and challenging problem which has attracted many researchers. Clustering stream data is facing two main challenges. First, as the data is continuously arriving with high rate and the computer storage capacity is limited, raw data can only be scaned in one pass. Second, stream data is always changing with time, so viewing a data stream as a set of static data can deteriorate the clustering quality. In fact, users are more concerned with the evolving behaviors of clusters which can help people making correct decisions. This paper proposes a density-grid based clustering algorithm, PKS-Stream-I, for stream data. It is an optimization of PKS-Stream in density detection period selection, sporadic grid detection and removal. Empirical results show the proposed method yields out better performance.
Keywords: Clustering; stream data; density-grid; Index Tree
Rights: © 2012 IEEE
DOI: 10.1109/PDCAT.2012.13
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