Optimization Algorithms for Scalable Stream Batch Clustering with k Estimation

Optimization Algorithms for Scalable Stream Batch Clustering with k Estimation

The increasing volume and velocity of the continuously generated data (data stream) challenge machine learning algorithms, which must evolve to fit real-world problems. The data stream clustering algorithms face issues such as the rapidly increasing volume of the data, the variety of the number of c...

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Bibliographic Details
Main Authors: Paulo Gustavo Lopes Cândido, Jonathan Andrade Silva, Elaine Ribeiro Faria, Murilo Coelho Naldi
Format: Article
Language:English
Published: MDPI AG 2022-06-01
Series:Applied Sciences
Subjects:
machine learning
clustering
data stream
massive parallel computation
Online Access:https://www.mdpi.com/2076-3417/12/13/6464

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https://www.mdpi.com/2076-3417/12/13/6464

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