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Clustering Algorithms for Spatial Big Data

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Computational Science and Its Applications – ICCSA 2017 (ICCSA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10407))

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Abstract

In our time people and devices constantly generate data. User activity generates data about needs and preferences as well as the quality of their experiences in different ways: i.e. streaming a video, looking at the news, searching for a restaurant or a an hotel, playing a game with others, making purchases, driving a car. Even when people put their devices in their pockets, the network is generating location and other data that keeps services running and ready to use. This rapid developments in the availability and access to data and in particular spatially referenced data in a different areas, has induced the need for better analysis techniques to understand the various phenomena. Spatial clustering algorithms, which groups similar spatial objects into classes, can be used for the identification of areas sharing common characteristics. The aim of this paper is to analyze the performance of three different clustering algorithms i.e. the Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN), the Fast Search by Density Peak (FSDP) algorithm and the classic K-means algorithm (K-Means) as regards the analysis of spatial big data. We propose a modification of the FSDP algorithm in order to improve its efficiency in large databases. The applications concern both synthetic data sets and satellite images.

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Authors and Affiliations

  1. DEAMS – Department of Economic, Business, Mathematic and Statistical Sciences “Bruno de Finetti”, University of Trieste, Tigor, 22, 34100, Trieste, Italy

    Gabriella Schoier & Caterina Gregorio

Authors
  1. Gabriella Schoier
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  2. Caterina Gregorio
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Corresponding author

Correspondence to Gabriella Schoier .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Trieste, Trieste, Italy

    Giuseppe Borruso

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  9. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  10. University of Trieste, Trieste, Italy

    Alfredo Cuzzocrea

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Schoier, G., Gregorio, C. (2017). Clustering Algorithms for Spatial Big Data. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10407. Springer, Cham. https://doi.org/10.1007/978-3-319-62401-3_41

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  • DOI: https://doi.org/10.1007/978-3-319-62401-3_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62400-6

  • Online ISBN: 978-3-319-62401-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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