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Clustering High-Dimensional Data

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Clustering High--Dimensional Data (CHDD 2012)

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Abstract

This chapter introduces the task of clustering, concerning the definition of a structure aggregating the data, and the challenges related to its application to the unsupervised analysis of high-dimensional data. In the recent literature, many approaches have been proposed for facing this problem, as the development of efficient clustering methods for high-dimensional data is is a great challenge for Machine Learning as it is of vital importance to obtain safer decision-making processes and better decisions from the nowadays available Big Data, that can mean greater operational efficiency, cost reduction and risk reduction.

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Authors and Affiliations

  1. Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi DIBRIS, Università di Genova, Genova, Italy

    Francesco Masulli & Stefano Rovetta

  2. Center for Biotechnology, Temple University, Philadelphia, USA

    Francesco Masulli

Authors
  1. Francesco Masulli
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  2. Stefano Rovetta
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Correspondence to Francesco Masulli .

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Editors and Affiliations

  1. DIBRIS, University of Genoa DIBRIS, Genoa, Italy

    Francesco Masulli

  2. University of Naples "Parthenope", Naples, Italy

    Alfredo Petrosino

  3. DIBRIS, University of Genoa, Genoa, Italy

    Stefano Rovetta

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Masulli, F., Rovetta, S. (2015). Clustering High-Dimensional Data. In: Masulli, F., Petrosino, A., Rovetta, S. (eds) Clustering High--Dimensional Data. CHDD 2012. Lecture Notes in Computer Science(), vol 7627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48577-4_1

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  • DOI: https://doi.org/10.1007/978-3-662-48577-4_1

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-48576-7

  • Online ISBN: 978-3-662-48577-4

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