SCLOPE: An algorithm for clustering data streams of categorical attributes

• Main Authors: ONG, Kok-Leong, LI, Wenyuan, NG, Wee-Keong, LIM, Ee Peng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2004
• Subjects: Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/1021; https://ink.library.smu.edu.sg/context/sis_research/article/2020/viewcontent/SCLOPE__An_algorithm_for_clustering_data_streams_of_categorical_attributes.pdf
• Institution: Singapore Management University

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.