SCLOPE: An Algorithm for Clustering Data Streams of Categorical Attributes
Conference Proceeding Article
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.
Databases and Information Systems
Data Management and Analytics
Data Warehousing and Knowledge Discovery: Proceedings of the 6th International Conference (DaWaK 2004)
City or Country
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. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1021