Mining Co-locations from Spatially Uncertain Data with Probability Intervals
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Uncertain data are inherent in many applications, and are usually described by precise probabilities. However, it is difficult to obtain precise probabilities over uncertain data in applications. This paper studies the problem of mining co-locations from spatially uncertain data with probability intervals. Firstly, it defines the possible world model with probability intervals, and proves that probability intervals of all possible worlds are feasible. Secondly, based on the feasible probability interval, it converts the probability intervals of possible worlds into point probabilities. Further, it defines the related concepts of probabilistic prevalent co-locations. Thirdly, it gives two lemmas for optimizing the computation of prevalence point probability of a candidate co-location. Further, it proves the closure property of prevalence point probability. Finally, the experiments on synthetic and real data sets show that the algorithms are effective and significant.
KeywordsUncertain data mining probability interval possible world probabilistic prevalent co-location
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