research-article

A polygon-based methodology for mining related spatial datasets

Authors:

Chun-Sheng Chen,

Vadeerat Rinsurongkawong,

Christoph F. EickAuthors Info & Claims

DMG '10: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Data Mining for Geoinformatics

Pages 1 - 8

https://doi.org/10.1145/1869890.1869891

Published: 02 November 2010 Publication History

Abstract

Polygons can serve an important role in the analysis of geo-referenced data as they provide a natural representation for particular types of spatial objects and in that they can be used as models for spatial clusters. This paper claims that polygon analysis is particularly useful for mining related, spatial datasets. A novel methodology for clustering polygons that have been extracted from different spatial datasets is proposed which consists of a meta clustering module that clusters polygons and a summary generation module that creates a final clustering from a polygonal meta clustering based on user preferences. Moreover, a density-based polygon clustering algorithm is introduced. Our methodology is evaluated in a real-world case study involving ozone pollution in Texas; it was able to reveal interesting relationships between different ozone hotspots and interesting associations between ozone hotspots and other meteorological variables.

References

[1]

Joshi, D., Samal, A. K., and Soh, L. K., 2009. Density-based clustering of polygons, in Proc. of IEEE Symposium on Computational Intelligence and Data Mining (Nashville, TN, USA, March 30 -- April 02, 2009).

[2]

Joshi, D., Samal, A. K., and Soh, L. K., 2009. A dissimilarity function for clustering geospatial polygons, in Proc. of the ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (Seattle, Wa, USA, November 04--06, 2009).

Digital Library

[3]

Zeng, Y., Tang, J., Garcia-Frias, J., and Gao, R. G., 2002. An adaptive meta-clustering approach: combining the information from different clustering results, in Proc. of IEEE Computer Society Conference on Bioinformatics (Palo Alto, CA, USA., August 14--16, 2002).

Digital Library

[4]

Gionis, A., Mannila, H., and Tsaparas, P., 2005. Clustering aggregation, in Proc. of the International Conference on Data Engineering (Tokyo, Japan, April 05--08, 2005).

Digital Library

[5]

Bansal, N., Blum, A., and Chawla, S., 2002. Correlation clustering, in Proc. of Symposium on Foundations of Computer Science (Vancouver, BC, Canada, November 16--19, 2002).

Digital Library

[6]

Caruana, R., Elhawary, M., Nguyen, N., and Smith, C., 2006. Meta clustering, in Proc of IEEE International Conference on Data Mining (Las Vegas, Nevada, USA, June 26--29, 2006).

Digital Library

[7]

Sander J., Ester M., Kriegel H.-P., and Xu X., 1998. Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and its Applications, Data Mining and Knowledge Discovery, Vol. 2, No. 2, 1998, pp. 169--194.

Digital Library

[8]

Ertoz, L., Steinback, M., and Kumar, V., 2003. Finding clusters of different sizes, shapes, and density in noisy, high dimensional data, in Proc. of SIAM International Conference on Data Mining (San Francisco, CA, USA, May 01--03, 2003).

[9]

Rinsurongkawong, V., Eick, C. F., 2010. Correspondence clustering: an approach to cluster multiple related datasets, in Proc. of Asia-Pacific Conference on Knowledge Discovery and Data Mining (Hyderabad, India, June 21--24, 2010).

Digital Library

[10]

Eick, C. F., Parmar, R., Ding, W., Stepinki, T., and Nicot, J. P., 2008. Finding regional co-location patterns for sets of continuous variables in spatial datasets, in Proc. of ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (Irvine, California, USA, November 05--07, 2008).

Digital Library

[11]

Zhang, Z., Huang, K., and Tan, T., 2006. Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes, in Proc. of International Conference on Pattern Recognition (Hong Kong, China, Augest 20--24, 2006).

Digital Library

[12]

Buchin, K., Buchin, M., and Wenk, C., 2006. Computing the Fréchet distance between simple polygons in polynomial time, In Proc. of Symposium on Computational Geometry (Sedona, Arizona, USA, June 05--07, 2006).

Digital Library

[13]

Rinsurongkawong, V. Chen, C. S., Eick, C. F., and Twa, M., 2010. Analyzing change in spatial data by utilizing polygon models, in Proc. of International Conference on Computing for Geospatial Research & Application (Washington, DC, USA, June 21--23, 2010).

Digital Library

[14]

Chen, C. S., Rinsurongkawong, V., Eick, C. F., Twa, M., 2009. Change analysis in spatial data by combining contouring algorithms with supervised density functions, in Proc. Of Asia-Pacific Conference on Knowledge Discovery and Data Mining (Bangkok, Thailand, April 27--30, 2009).

Digital Library

[15]

Duckham, M., Kulik, L., Worboys, M., and Galton, A., 2008. Efficient generation of simple polygons for characterizing the shape of a set of points in the plane, Pattern Recognition. 41, pp. 3224--3236.

Digital Library

[16]

Edelsbrunner, H., Kirkpatrick, D. G., and Seidel, R., 1983. On the shape of a set of points in the plane, IEEE Transactions on Information Theory, vol. IT-29, no. 4, pp. 551--558.

Digital Library

[17]

Marx, Z., Dagan, I., Buhmann, J. M., and Shamir, E., 2002. Coupled clustering: a method for detecting structural correspondence, Journal of Machine Learning Research, pp. 747--780.

Digital Library

[18]

Dhillon, I. S., 2001. Co-clustering documents and words using bipartite spectral graph partitioning, in Proc. of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, California, USA, August 26--29, 2001).

Digital Library

[19]

Cheng, Y., Church, C. M., 2000. Biclustering of Expression Data, in Proc. of International Conference on Intelligent Systems for Molecular Biology.

Digital Library

[20]

Hangouet, J., 1995. Computing of the Hausdorff distance between plane vector polylines, in Proc. of Symposium on Computer-Assisted Cartography.

[21]

Atallah M. J., Ribeiro, C. C., and Lifschitz, S., 1991. Computing some distance functions between polygons, Pattern Recognition, Vol. 24, Issue 8, pp. 775--781, 1991.

Digital Library

[22]

Texas Commission on Environmental Quality, http://www.tceq.state.tx.us

[23]

MacQueen, J. B., 1967. Some methods for classification and analysis of multivariate observations, in Proc. Of Berkeley Symposium on Mathematical Statistics and Probability (Berkeley, CA, USA, June 21 - July 18, 1967).

[24]

American Lung Association, http://www.lungusa.org/

[25]

Cressie, N., 1993, Statistics for spatial data. New York: Wiley.

Cited By

You G(2021)Spatiotemporal Data-Adaptive Clustering Algorithm: An Intelligent Computational Technique for City Big DataAnnals of the American Association of Geographers10.1080/24694452.2021.1935207(1-18)Online publication date: 17-Aug-2021
https://doi.org/10.1080/24694452.2021.1935207
Abubahia ABader-El-Den MHaig E(2021)Evolutionary Computation Approach for Spatial Workload BalancingIntelligent Computing10.1007/978-3-030-80126-7_38(524-542)Online publication date: 7-Jul-2021
https://doi.org/10.1007/978-3-030-80126-7_38
Jarraya SMasmoudi MHammami M(2020)Compound Emotion Recognition of Autistic Children During Meltdown Crisis Based on Deep Spatio-Temporal Analysis of Facial Geometric FeaturesIEEE Access10.1109/ACCESS.2020.29866548(69311-69326)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2986654
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Index Terms

A polygon-based methodology for mining related spatial datasets
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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cover image ACM Conferences

DMG '10: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Data Mining for Geoinformatics

November 2010

64 pages

ISBN:9781450304306

DOI:10.1145/1869890

General Chairs:
Jessica Lin
George Mason University
,
Guido Cervone
George Mason University
,
Nigel Waters
George Mason University

Copyright © 2010 ACM.

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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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 November 2010

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GIS '10

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SIGSPATIAL

GIS '10: 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2, 2010

California, San Jose

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Cited By

You G(2021)Spatiotemporal Data-Adaptive Clustering Algorithm: An Intelligent Computational Technique for City Big DataAnnals of the American Association of Geographers10.1080/24694452.2021.1935207(1-18)Online publication date: 17-Aug-2021
https://doi.org/10.1080/24694452.2021.1935207
Abubahia ABader-El-Den MHaig E(2021)Evolutionary Computation Approach for Spatial Workload BalancingIntelligent Computing10.1007/978-3-030-80126-7_38(524-542)Online publication date: 7-Jul-2021
https://doi.org/10.1007/978-3-030-80126-7_38
Jarraya SMasmoudi MHammami M(2020)Compound Emotion Recognition of Autistic Children During Meltdown Crisis Based on Deep Spatio-Temporal Analysis of Facial Geometric FeaturesIEEE Access10.1109/ACCESS.2020.29866548(69311-69326)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2986654
Jarraya SMasmoudi MHammami M(2020)A comparative study of Autistic Children Emotion recognition based on patio-Temporal and Deep analysis of facial expressions features during a Meltdown CrisisMultimedia Tools and Applications10.1007/s11042-020-09451-yOnline publication date: 25-Aug-2020
https://doi.org/10.1007/s11042-020-09451-y
Wang SEick C(2017)A data mining framework for environmental and geo-spatial data analysisInternational Journal of Data Science and Analytics10.1007/s41060-017-0075-95:2-3(83-98)Online publication date: 30-Sep-2017
https://doi.org/10.1007/s41060-017-0075-9
Renteria-Agualimpia WLopez-Pellicer FLacasta JZarazaga-Soria FMuro-Medrano P(2016)Improving the geospatial consistency of digital libraries metadataJournal of Information Science10.1177/016555151559736442:4(507-523)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1177/0165551515597364
Wang SCai TEick CNascimento MRenz MEmrich TMouratidis KZüfle A(2014)New Clustering and Analyzing Technique for Mining Multi-source Enriched Geo-spatial DataProceedings of Workshop on Managing and Mining Enriched Geo-Spatial Data10.1145/2619112.2619118(1-6)Online publication date: 22-Jun-2014
https://dl.acm.org/doi/10.1145/2619112.2619118
Wang SEick C(2014)A polygon-based clustering and analysis framework for mining spatial datasetsGeoinformatica10.1007/s10707-013-0190-218:3(569-594)Online publication date: 1-Jul-2014
https://dl.acm.org/doi/10.1007/s10707-013-0190-2
Akdag FEick CChen G(2014)Creating Polygon Models for Spatial ClustersFoundations of Intelligent Systems10.1007/978-3-319-08326-1_50(493-499)Online publication date: 2014
https://doi.org/10.1007/978-3-319-08326-1_50
Renteria-Agualimpia WLópez-Pellicer FLacasta JZarazaga-Soria FMuro-Medrano PCamacho DAkerkar RRodriguez Moreno M(2013)Identifying hidden geospatial resources in cataloguesProceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics10.1145/2479787.2479812(1-7)Online publication date: 12-Jun-2013
https://dl.acm.org/doi/10.1145/2479787.2479812
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