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Title
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Cohesion based co-location pattern mining
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Author
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Abstract
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| Because of a wide range of applications, e.g., GPS applications and location based services, spatial pattern discovery is an important task in data mining. A co-location pattern is defined as a subset of spatial items whose instances are often located together in spatial proximity. Current co-location mining algorithms are unable to quantify the spatial proximity of a co-location pattern. We propose a co-location pattern miner aiming to discover co-location patterns in a multidimensional spatial structure by measuring the cohesion of a pattern. We present two ways to build the co-location pattern miner, FromOne and FromAll, in an attempt to find a balance between accuracy and runtime. Additionally, we propose a method named Fre-ball to transform a structure into a transaction database, after which any existing itemset mining algorithm can be used to find the co-location patterns. An experimental evaluation shows that FromOne and Fre-ball are more efficient than existing methods. The usefulness of our methods is demonstrated by applying them on the publicly available geographical data of the city of Antwerp in Belgium. |
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Language
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English
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Source (book)
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IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2015
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Publication
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S.l.
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IEEE
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2015
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DOI
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10.1109/DSAA.2015.7344839
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Volume/pages
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p. 1-10
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ISI
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000380468400059
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Full text (Publisher's DOI)
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