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Recent Advances on Distributed Unsupervised Learning

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 54))

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Abstract

Distributed machine learning is a problem of inferring a desired relation when the training data is distributed throughout a network of agents (e.g. sensor networks, robot swarms, etc.). A typical problem of unsupervised learning is clustering, that is grouping patterns based on some similarity/dissimilarity measures. Provided they are highly scalable, fault-tolerant and energy efficient, clustering algorithms can be adopted in large-scale distributed systems. This work surveys the state-of-the-art in this field, presenting algorithms that solve the distributed clustering problem efficiently, with particular attention to the computation and clustering criteria.

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Authors and Affiliations

  1. Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome “La Sapienza”, Via Eudossiana 18, 00184, Rome, Italy

    Antonello Rosato, Rosa Altilio & Massimo Panella

Authors
  1. Antonello Rosato
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  2. Rosa Altilio
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  3. Massimo Panella
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Corresponding author

Correspondence to Massimo Panella .

Editor information

Editors and Affiliations

  1. Computer Science Department, University of Milano, Milano, Italy

    Simone Bassis

  2. Department of Psychology, Seconda Università di Napoli and IIASS, Caserta, Italy

    Anna Esposito

  3. Dept. of Info., Mathematics, Ele & Trans, Univ. Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

  4. Dip. Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy

    Eros Pasero

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Rosato, A., Altilio, R., Panella, M. (2016). Recent Advances on Distributed Unsupervised Learning. In: Bassis, S., Esposito, A., Morabito, F., Pasero, E. (eds) Advances in Neural Networks. WIRN 2015. Smart Innovation, Systems and Technologies, vol 54. Springer, Cham. https://doi.org/10.1007/978-3-319-33747-0_8

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  • DOI: https://doi.org/10.1007/978-3-319-33747-0_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33746-3

  • Online ISBN: 978-3-319-33747-0

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