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An Evolving Fuzzy Model to Determine an Optimal Number of Data Stream Clusters
International Journal of Fuzzy Logic and Intelligent Systems
Data streams are a modern type of data that differ from traditional data in various characteristics: their indefinite size, high access, and concept drift due to their origin in non-stationary environments. Data stream clustering aims to split these data samples into significant clusters, depending on their similarity. The main drawback of data stream clustering algorithms is the large number of clusters they produce. Therefore, determining an optimal number of clusters is an importantdoi:10.5391/ijfis.2022.22.3.267 fatcat:5lxsn4tjcngjreqiv43l4qllwi