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• W2895845398 abstract "Approaches that combine streaming algorithms and distributed computing have potential to deal with voluminous and high-speed data streams. Considering the data stream clustering task, also an important issue needs to be addressed, estimate the number of clusters dynamically, since it may vary due to concept drift. This work proposes three evolutionary-based algorithms to overcome these requirements. They are based in the discretized stream model, where the sequential batches of objects are distributed and processed in a parallel way using the MapReduce model. The proposed algorithms achieve superior experimental results, either in quality and processing time, overcoming the state-of-the-art." @default.
• W2895845398 created "2018-10-26" @default.
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• W2895845398 date "2018-07-01" @default.
• W2895845398 modified "2023-09-27" @default.
• W2895845398 title "Scalable Batch Stream Clustering with k Estimation" @default.
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• W2895845398 doi "https://doi.org/10.1109/cec.2018.8477668" @default.
• W2895845398 hasPublicationYear "2018" @default.
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