A Comparative Study of Density-based Clustering Algorithms on Data Streams: Micro-clustering Approaches

  • Amineh AminiEmail author
  • Teh Ying Wah
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 110)


Clustering data streams is a challenging problem in mining data streams. Data streams need to be read by a clustering algorithm in a single pass with limited time, and memory whereas they may change over time. Different clustering algorithms have been developed for data streams. Density-based algorithms are a remarkable group in clustering data that can find arbitrary shape clusters, and handle the outliers as well. In recent years, density-based clustering algorithms are adopted for data streams. However, in clustering data streams, it is impossible to record all data streams. Micro-clustering is a summarization method used to record synopsis information about data streams. Various algorithms apply micro-clustering methods for clustering data streams. In this paper, we will concentrate on the density-based clustering algorithms that use micro-clustering methods for clustering and we refer them as density-micro clustering algorithms. We review the algorithms in details and compare them based on different characteristics.


Data streams Density-based clustering Micro-cluster 


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© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Information System, Faculty of Computer Science and Information TechnologyUniversity of Malaya (UM)Kuala LumpurMalaysia

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