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W. Ng and M. Dash, “Discovery of Frequent Patterns in Transactional Data Streams,” Transactions on Large-Scale Data-and Knowledge-Centered Systems II, Vol. 6380 of Lecture Notes in Computer Science, Springer, Berlin/ Heidelberg, 2010, pp. 1-30.

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.