Online Clustering Algorithms
In this chapter, we show how we can extend the algorithms in Chapter 3 and allow them to learn in online mode. The aim of this chapter is to allow prototypes to learn in a different way, online, to that in batch mode. This may lead to different results due to the different behavior in the learning process. Furthermore, a limitation of batch processing algorithms is that they cannot readily respond to new data if the data only becomes available over time. Thus we construct a new set of online clustering algorithms based on extension of some of the algorithms in Chapter 3 and sharing the same performance functions.
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© 2009 Springer-Verlag Berlin Heidelberg
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Barbakh, W.A., Wu, Y., Fyfe, C. (2009). Online Clustering Algorithms and Reinforcement Learning. In: Non-Standard Parameter Adaptation for Exploratory Data Analysis. Studies in Computational Intelligence, vol 249. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04005-4_6
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DOI: https://doi.org/10.1007/978-3-642-04005-4_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04004-7
Online ISBN: 978-3-642-04005-4
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