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L. Wan, W. K. Ng, X. H. Dang, P. S. Yu and K. Zhang, “Density-Based Clustering of Data Streams at Multiple Resolutions,” ACM Transactions Knowledge Discovery Data, Vol. 3, No. 3, 2009, pp. 1-28. http://dx.doi.org/10.1145/1552303.1552307

has been cited by the following article:

• TITLE: LeaDen-Stream: A Leader Density-Based Clustering Algorithm over Evolving Data Stream

  AUTHORS: Amineh Amini, Teh Ying Wah

  KEYWORDS: Evolving Data Streams; Density-Based Clustering; Micro Cluster; Mini-Micro Cluster

  JOURNAL NAME: Journal of Computer and Communications, Vol.1 No.5, November 8, 2013

  ABSTRACT: Clustering evolving data streams is important to be performed in a limited time with a reasonable quality. The existing micro clustering based methods do not consider the distribution of data points inside the micro cluster. We propose LeaDen-Stream (Leader Density-based clustering algorithm over evolving data Stream), a density-based clustering algorithm using leader clustering. The algorithm is based on a two-phase clustering. The online phase selects the proper mini-micro or micro-cluster leaders based on the distribution of data points in the micro clusters. Then, the leader centers are sent to the offline phase to form final clusters. In LeaDen-Stream, by carefully choosing between two kinds of micro leaders, we decrease time complexity of the clustering while maintaining the cluster quality. A pruning strategy is also used to filter out real data from noise by introducing dense and sparse mini-micro and micro-cluster leaders. Our performance study over a number of real and synthetic data sets demonstrates the effectiveness and efficiency of our method.