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Mining condensed spatial co-location patterns

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Monica WachowiczAuthors Info & Claims

MobiGIS '15: Proceedings of the Fourth ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems

Pages 84 - 87

https://doi.org/10.1145/2834126.2834135

Published: 03 November 2015 Publication History

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Abstract

The discovery of co-location patterns among spatial events is an important task in spatial data mining. We introduce a new kind of spatial co-location patterns, named condensed spatial co-location patterns, that can be considered as a lossy compressed representation of all the co-location patterns. Each condensed pattern is the representative, and a superset, of a group of spatial co-location patterns in the full set of patterns such that the difference between the interestingness measure of the representative and the measures of the patterns belonging to the associated group are negligible. Our preliminary experiments show that condensed spatial co-location patterns are less sensitive to parameter changes and more robust in presence of missing data than closed spatial co-location patterns.

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Cited By

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Wu PWang LYang PHu X(2024)Representative co-location pattern post-mining based on maximal row instances representation modelKnowledge-Based Systems10.1016/j.knosys.2024.112237301:COnline publication date: 9-Oct-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.112237
Wang LFang YZhou LWang LFang YZhou L(2022)Vector-Degree: A General Similarity Measure for Co-location PatternsPreference-based Spatial Co-location Pattern Mining10.1007/978-981-16-7566-9_11(265-284)Online publication date: 4-Jan-2022
https://doi.org/10.1007/978-981-16-7566-9_11
Wu PWang LZou M(2019)Vector-Degree: A General Similarity Measure for Co-location Patterns2019 IEEE International Conference on Big Knowledge (ICBK)10.1109/ICBK.2019.00045(281-288)Online publication date: Nov-2019
https://doi.org/10.1109/ICBK.2019.00045

Index Terms

Mining condensed spatial co-location patterns
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems

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MobiGIS '15: Proceedings of the Fourth ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems

November 2015

95 pages

ISBN:9781450339773

DOI:10.1145/2834126

Editors:
Chi-Yin Chow
City University of Hong Kong, Hong Kong
,
Maria Luisa Damiani
University of Milan, Italy
,
Shashi Shekhar
University of Minnesota - Twin Cities

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

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Published: 03 November 2015

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SIGSPATIAL'15: 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 3, 2015

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Wu PWang LYang PHu X(2024)Representative co-location pattern post-mining based on maximal row instances representation modelKnowledge-Based Systems10.1016/j.knosys.2024.112237301:COnline publication date: 9-Oct-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.112237
Wang LFang YZhou LWang LFang YZhou L(2022)Vector-Degree: A General Similarity Measure for Co-location PatternsPreference-based Spatial Co-location Pattern Mining10.1007/978-981-16-7566-9_11(265-284)Online publication date: 4-Jan-2022
https://doi.org/10.1007/978-981-16-7566-9_11
Wu PWang LZou M(2019)Vector-Degree: A General Similarity Measure for Co-location Patterns2019 IEEE International Conference on Big Knowledge (ICBK)10.1109/ICBK.2019.00045(281-288)Online publication date: Nov-2019
https://doi.org/10.1109/ICBK.2019.00045

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Mining Co-Location Patterns with Rare Events from Spatial Data Sets

Discovering spatial contrast and common sets with statistically significant co-location patterns

Mining frequent geographic patterns with knowledge constraints

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