Zonal Co-location Pattern Discovery with Dynamic Parameters

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Zonal Co-location Pattern Discovery with Dynamic Parameters

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by Mete Celik , James M. Kang , Shashi Shekhar

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@MISC{Celik_zonalco-location,
    author = {Mete Celik and James M. Kang and Shashi Shekhar},
    title = {Zonal Co-location Pattern Discovery with Dynamic Parameters},
    year = {}
}

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Abstract

Zonal co-location patterns represent subsets of featuretypes that are frequently located in a subset of space (i.e., zone). Discovering zonal spatial co-location patterns is an important problem with many applications in areas such as ecology, public health, and homeland defense. However, discovering these patterns with dynamic parameters (i.e., repeated specification of zone and interest measure values according to user preferences) is computationally complex due to the repetitive mining process. Also, the set of candidate patterns is exponential in the number of feature types, and spatial datasets are huge. Previous studies have focused on discovering global spatial co-location patterns with a fixed interest measure threshold. In this paper, we propose an indexing structure for co-location patterns and propose algorithms (Zoloc-Miner) to discover zonal colocation patterns efficiently for dynamic parameters. Extensive experimental evaluation shows our proposed approaches are scalable, efficient, and outperform naïve alternatives. 1.

Keyphrases

dynamic parameter zonal co-location pattern discovery ve alternative many application spatial datasets user preference candidate pattern global spatial co-location pattern previous study important problem zonal colocation pattern homeland defense propose algorithm co-location pattern public health repetitive mining process feature type zonal co-location pattern represent subset extensive experimental evaluation interest measure zonal spatial co-location pattern indexing structure fixed interest measure

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