Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
9-2004
Abstract
Clustering is a difficult problem especially when we consider the task in the context of a data stream of categorical attributes. In this paper, we propose SCLOPE, a novel algorithm based on CLOPE's intuitive observation about cluster histograms. Unlike CLOPE however, our algorithm is very fast and operates within the constraints of a data stream environment. In particular, we designed SCLOPE according to the recent CluStream framework. Our evaluation of SCLOPE shows very promising results. It consistently outperforms CLOPE in speed and scalability tests on our data sets while maintaining high cluster purity; it also supports cluster analysis that other algorithms in its class do not.
Discipline
Databases and Information Systems
Publication
Data Warehousing and Knowledge Discovery: Proceedings of the 6th International Conference (DaWaK 2004)
Volume
3181
First Page
209
Last Page
218
ISBN
9783540300762
Identifier
10.1007/978-3-540-30076-2_21
Publisher
Springer Verlag
City or Country
Zaragoza, Spain
Citation
ONG, Kok-Leong; LI, Wenyuan; NG, Wee-Keong; and LIM, Ee Peng.
SCLOPE: An algorithm for clustering data streams of categorical attributes. (2004). Data Warehousing and Knowledge Discovery: Proceedings of the 6th International Conference (DaWaK 2004). 3181, 209-218.
Available at: https://ink.library.smu.edu.sg/sis_research/1021
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://dx.doi.org/10.1007/978-3-540-30076-2_21