Abstract
Data stream mining has recently been studied extensively in the literature. Many clustering algorithms were proposed to handle massive streams of data. However, many of these algorithms may not be as efficient as one desires for data streams, as they typically require a number of iterations in their implementations. In this paper, we will propose a new data stream clustering algorithm, based on the fast density-peak-search method. It does not require any iterations in its implementation, and therefore is most suitable for large streams of data. The comparisons of numerical illustration as well as a real example will be made with other alternative data stream algorithms.
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