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ESTATE: Strategy for Exploring Labeled Spatial Datasets Using Association Analysis

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Discovery Science (DS 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6332))

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Abstract

We propose an association analysis-based strategy for exploration of multi-attribute spatial datasets possessing naturally arising classification. Proposed strategy, ESTATE (Exploring Spatial daTa Association patTErns), inverts such classification by interpreting different classes found in the dataset in terms of sets of discriminative patterns of its attributes. It consists of several core steps including discriminative data mining, similarity between transactional patterns, and visualization. An algorithm for calculating similarity measure between patterns is the major original contribution that facilitates summarization of discovered information and makes the entire framework practical for real life applications. Detailed description of the ESTATE framework is followed by its application to the domain of ecology using a dataset that fuses the information on geographical distribution of biodiversity of bird species across the contiguous United States with distributions of 32 environmental variables across the same area.

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Author information

Authors and Affiliations

  1. Lunar and Planetary Institute, Houston, TX, 77058, USA

    Tomasz F. Stepinski & Josue Salazar

  2. Department of Computer Science, University of Massachusetts Boston, Boston, MA, 02125, USA

    Wei Ding

  3. US Environmental Protection Agency, Corvallis, OR, 97333, USA

    Denis White

Authors
  1. Tomasz F. Stepinski
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  2. Josue Salazar
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  3. Wei Ding
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  4. Denis White
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Waikato, Hamilton, New Zealand

    Bernhard Pfahringer

  2. Department of Computer Science, The University of Waikato, Private Bag 3105, 3240, Hamilton, New Zealand

    Geoff Holmes

  3. School of Computer Science and Engineering, The University of New South Wales, 2052, Sydney, Australia

    Achim Hoffmann

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Stepinski, T.F., Salazar, J., Ding, W., White, D. (2010). ESTATE: Strategy for Exploring Labeled Spatial Datasets Using Association Analysis. In: Pfahringer, B., Holmes, G., Hoffmann, A. (eds) Discovery Science. DS 2010. Lecture Notes in Computer Science(), vol 6332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16184-1_23

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  • DOI: https://doi.org/10.1007/978-3-642-16184-1_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16183-4

  • Online ISBN: 978-3-642-16184-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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