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Scalable and High Performing Learning and Mining in Large-Scale Networked Environments: A State-of-the-art Survey

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Book cover Transactions on Computational Collective Intelligence X

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 7776))

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Abstract

Scalability is a major issue in the application of machine learning and data mining to large-scale networked environments. While there has been important progress in the learnability of models for medium-sized datasets, there is still much challenge in facing large-scale systems. In particular, with the evolution of distributed and networked environments, the complexity of the learning and mining process has now grown due to the possibility to integrating more data in the learning process. This paper provides a survey on the state-of-the-art on the methods and algorithms to enhance scalability of machine learning and data mining for large-scale networked systems.

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Authors and Affiliations

  1. Department of Computer Science, Polytechnic University of Tirana, Albania

    Evis Trandafili

  2. Department of Computer Science, University of New York in Tirana, Albania

    Marenglen Biba

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  1. Evis Trandafili
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Editors and Affiliations

  1. Wroclaw University of Technology, Wyb. Wyspiánskiego 27, 50-370, Wroclaw, Poland

    Ngoc-Thanh Nguyen

  2. Institute of Computer Science, Cracow University of Technology, ul. Warszawska 24, 31-155, Cracow, Poland

    Joanna Kołodziej

  3. Institute of Computer Science, Cracow University of Technology , ul. Warszawska 24, 31-155, Cracow, Poland

    Tadeusz Burczyński

  4. University of New York in Tirana, Rr. Komuna e Parisit, Tirana, Albania

    Marenglen Biba

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Trandafili, E., Biba, M. (2013). Scalable and High Performing Learning and Mining in Large-Scale Networked Environments: A State-of-the-art Survey. In: Nguyen, NT., Kołodziej, J., Burczyński, T., Biba, M. (eds) Transactions on Computational Collective Intelligence X. Lecture Notes in Computer Science, vol 7776. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38496-7_11

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  • DOI: https://doi.org/10.1007/978-3-642-38496-7_11

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