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WIREs Data Mining Knowl Discov
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A Delaunay diagram‐based Min–Max CP‐Tree algorithm for Spatial Data Analysis

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Co‐location patterns are the subsets of Boolean spatial features whose instances are often located in close geographic proximity. Neighborhood is a major challenge and a key part of spatial co‐location pattern mining. In existing conventional models, the neighborhood was defined by the user which is not suitable for massive data set. The idea of this paper is to improve the performance of co‐location mining by proposing novel neighborhood model and effective co‐location algorithm for spatial data analysis. The first methodology is to model the neighborhood of spatial data by using Delaunay diagram geometry approach. Delaunay‐based neighborhood model finds the neighborhoods dynamically and avoids user‐based neighborhood. The second methodology is to present novel efficient Min–Max CP‐Tree algorithm to discover precise co‐location patterns from spatial data. The proposed co‐location mining algorithm is effective and efficient for complex landslide spatial data. WIREs Data Mining Knowl Discov 2015, 5:142–154. doi: 10.1002/widm.1151 This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Application Areas > Data Mining Software Tools Technologies > Association Rules
Spatial point data.
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Co‐location mining approaches performance based on execution time.
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Neighborhood models performance based on neighborhood degree.
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Spatial neighborhood models accuracy for various data size.
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Number of co‐location pattern for various neighborhood models.
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Delaunay diagram for landslide data features.
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Landslide data features.
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Landslide data.
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CP‐Tree for various neighborhood transactions.
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Delaunay diagram of the spatial point data.
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Technologies > Association Rules
Application Areas > Data Mining Software Tools
Algorithmic Development > Spatial and Temporal Data Mining

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