research-article

Spatial outlier detection: random walk based approaches

Authors:

Feng ChenAuthors Info & Claims

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

Pages 370 - 379

https://doi.org/10.1145/1869790.1869841

Published: 02 November 2010 Publication History

Abstract

A spatial outlier is a spatially referenced object whose non-spatial attributes are very different from those of its spatial neighbors. Spatial outlier detection has been an important part of spatial data mining and attracted attention in the past decades. Numerous SOD (Spatial Outlier Detection) approaches have been proposed. However, in these techniques, there exist the problems of masking and swamping. That is, some spatial outliers can escape the identification, and normal objects can be erroneously identified as outliers. In this paper, two Random walk based approaches, RW-BP (Random Walk on Bipartite Graph) and RW-EC (Random Walk on Exhaustive Combination), are proposed to detect spatial outliers. First, two different weighed graphs, a BP (Bipartite graph) and an EC (Exhaustive Combination), are modeled based on the spatial and/or non-spatial attributes of the spatial objects. Then, random walk techniques are utilized on the graphs to compute the relevance scores between the spatial objects. Using the analysis results, the outlier scores are computed for each object and the top k objects are recognized as outliers. Experiments conducted on the synthetic and real datasets demonstrated the effectiveness of the proposed approaches.

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Cited By

Wang YCao XLi Y(2022)Unsupervised Outlier Detection for Mixed-Valued Dataset Based on the Adaptive k-Nearest Neighbor Global NetworkIEEE Access10.1109/ACCESS.2022.316148110(32093-32103)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3161481
Karale ALazarova MKoleva PPoulkov V(2021)MEOD: Memory-Efficient Outlier Detection on Streaming DataSymmetry10.3390/sym1303045813:3(458)Online publication date: 12-Mar-2021
https://doi.org/10.3390/sym13030458
Karale ALazarova MKoleva PPoulkov V(2021)Advanced Memory Efficient Outlier Detection Approach for Streaming Data using Swarm Optimization2021 44th International Conference on Telecommunications and Signal Processing (TSP)10.1109/TSP52935.2021.9522667(346-351)Online publication date: 26-Jul-2021
https://doi.org/10.1109/TSP52935.2021.9522667
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Index Terms

Spatial outlier detection: random walk based approaches
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

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

November 2010

566 pages

ISBN:9781450304283

DOI:10.1145/1869790

General Chairs:
Divyakant Agrawal
University of California at Santa Barbara
,
Pusheng Zhang
Microsoft Corporation
,
Program Chairs:
Amr El Abbadi
University of California, Santa Barbara
,
Mohamed Mokbel
University of Minnesota

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

November 2 - 5, 2010

California, San Jose

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Cited By

Wang YCao XLi Y(2022)Unsupervised Outlier Detection for Mixed-Valued Dataset Based on the Adaptive k-Nearest Neighbor Global NetworkIEEE Access10.1109/ACCESS.2022.316148110(32093-32103)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3161481
Karale ALazarova MKoleva PPoulkov V(2021)MEOD: Memory-Efficient Outlier Detection on Streaming DataSymmetry10.3390/sym1303045813:3(458)Online publication date: 12-Mar-2021
https://doi.org/10.3390/sym13030458
Karale ALazarova MKoleva PPoulkov V(2021)Advanced Memory Efficient Outlier Detection Approach for Streaming Data using Swarm Optimization2021 44th International Conference on Telecommunications and Signal Processing (TSP)10.1109/TSP52935.2021.9522667(346-351)Online publication date: 26-Jul-2021
https://doi.org/10.1109/TSP52935.2021.9522667
Zhang SWan J(2019)Anomaly Region Detection Based on DMSTInternational Journal of Data Warehousing and Mining10.4018/IJDWM.201901010315:1(39-57)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.4018/IJDWM.2019010103
Djenouri YZimek A(2018)Outlier Detection in Urban Traffic DataProceedings of the 8th International Conference on Web Intelligence, Mining and Semantics10.1145/3227609.3227692(1-12)Online publication date: 25-Jun-2018
https://dl.acm.org/doi/10.1145/3227609.3227692
Rottoli GMerlino HGarcía-Martínez R(2018)Knowledge Discovery Process for Detection of Spatial OutliersRecent Trends and Future Technology in Applied Intelligence10.1007/978-3-319-92058-0_6(57-68)Online publication date: 30-May-2018
https://doi.org/10.1007/978-3-319-92058-0_6
Zimek AFilzmoser P(2018)There and back again: Outlier detection between statistical reasoning and data mining algorithmsWIREs Data Mining and Knowledge Discovery10.1002/widm.12808:6Online publication date: 20-Aug-2018
https://doi.org/10.1002/widm.1280
Zheng GBrantley SLauvaux TLi ZMatwin SYu SFarooq F(2017)Contextual Spatial Outlier Detection with Metric LearningProceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/3097983.3098143(2161-2170)Online publication date: 13-Aug-2017
https://dl.acm.org/doi/10.1145/3097983.3098143
Shi YDeng MYang XLiu Q(2016)A Spatial Anomaly Points and Regions Detection Method Using Multi‐Constrained Graphs and Local DensityTransactions in GIS10.1111/tgis.1220821:2(376-405)Online publication date: 27-Jun-2016
https://doi.org/10.1111/tgis.12208
Schubert EWeiler MZimek A(2015)Outlier Detection and Trend DetectionProceedings of the 2015 IEEE International Conference on Data Mining Workshop (ICDMW)10.1109/ICDMW.2015.79(40-46)Online publication date: 14-Nov-2015
https://dl.acm.org/doi/10.1109/ICDMW.2015.79
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