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- W2012886963 abstract "Mining co-location patterns from spatial databases may reveal types of spatial features likely located as neighbors in space. In this paper, we address the problem of mining confident co-location rules without a support threshold. First, we propose a novel measure called the maximal participation index. We show that every confident co-location rule corresponds to a co-location pattern with a high maximal participation index value. Second, we show that the maximal participation index is non-monotonic, and thus the conventional Apriori-like pruning does not work directly. We identify an interesting weak monotonic property for the index and develop efficient algorithms to mine confident co-location rules. An extensive performance study shows that our method is both effective and efficient for large spatial databases." @default.
- W2012886963 created "2016-06-24" @default.
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- W2012886963 date "2003-03-09" @default.
- W2012886963 modified "2023-10-18" @default.
- W2012886963 title "Mining confident co-location rules without a support threshold" @default.
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- W2012886963 doi "https://doi.org/10.1145/952532.952630" @default.
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