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Contraction Clustering (RASTER)

A Big Data Algorithm for Density-Based Clustering in Constant Memory and Linear Time

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Book cover Machine Learning, Optimization, and Big Data (MOD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10710))

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Abstract

Clustering is an essential data mining tool for analyzing and grouping similar objects. In big data applications, however, many clustering methods are infeasible due to their memory requirements or runtime complexity. (RASTER) is a linear-time algorithm for identifying density-based clusters. Its coefficient is negligible as it depends neither on input size nor the number of clusters. Its memory requirements are constant. Consequently, RASTER is suitable for big data applications where the size of the data may be huge. It consists of two steps: (1) a contraction step which projects objects onto tiles and (2) an agglomeration step which groups tiles into clusters. Our algorithm is extremely fast. In single-threaded execution on a contemporary workstation, it clusters ten million points in less than 20 s—when using a slow interpreted programming language like Python. Furthermore, RASTER is easily parallelizable.

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Notes

  1. 1.

    The chosen shorthand may not be immediately obvious: RASTER operates on an implied grid. Resulting clusters can be made to look similar to the dot matrix structure of a raster graphics image. Furthermore, the name RASTER is an agglomerated contraction of the words and

  2. 2.

    On a contemporary workstation with 16 GB RAM, the scikit-learn implementation of DBSCAN cannot even handle one million data points.

  3. 3.

    We have identified tens of thousands of clusters with RASTER in a huge real-world data set, which shows that k-means clustering would have been highly unsuitable.

  4. 4.

    Reference implementations in several programming languages are available at https://gitlab.com/fraunhofer_chalmers_centre/contraction_clustering_raster.

  5. 5.

    In case it is not obvious why this is in linear time: For each row in a grid, take the current and next row into account. Start, for instance, with the tile in the top left corner and take its right neighbor as well as the two tiles adjacent in the next row into account.

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Acknowledgments

This research was supported by the project Fleet telematics big data analytics for vehicle usage modeling and analysis (FUMA) in the funding program FFI: Strategic Vehicle Research and Innovation (DNR 2016-02207), which is administered by VINNOVA, the Swedish Government Agency for Innovation Systems.

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Authors and Affiliations

  1. Fraunhofer-Chalmers Research Centre for Industrial Mathematics, Chalmers Science Park, 412 88, Gothenburg, Sweden

    Gregor Ulm, Emil Gustavsson & Mats Jirstrand

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  1. Gregor Ulm
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  2. Emil Gustavsson
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  3. Mats Jirstrand
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Correspondence to Gregor Ulm .

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Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. University of Catania, Catania, Italy

    Giovanni Giuffrida

  4. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Ulm, G., Gustavsson, E., Jirstrand, M. (2018). Contraction Clustering (RASTER). In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R. (eds) Machine Learning, Optimization, and Big Data. MOD 2017. Lecture Notes in Computer Science(), vol 10710. Springer, Cham. https://doi.org/10.1007/978-3-319-72926-8_6

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  • DOI: https://doi.org/10.1007/978-3-319-72926-8_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72925-1

  • Online ISBN: 978-3-319-72926-8

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