A Probabilistic Method for Mining Sequential Rules from Sequences of LBS Cloaking Regions

A Probabilistic Method for Mining Sequential Rules from Sequences of LBS Cloaking Regions

Haitao Zhang, Zewei Chen, Zhao Liu, Yunhong Zhu, Chenxue Wu
Copyright: © 2017 |Pages: 15
DOI: 10.4018/IJDWM.2017010102
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Abstract

Analyzing large-scale spatial-temporal anonymity sets can benefit many LBS applications. However, traditional spatial-temporal data mining algorithms cannot be used for anonymity datasets because the uncertainty of anonymity datasets renders those algorithms ineffective. In this paper, the authors adopt the uncertainty of anonymity datasets and propose a probabilistic method for mining sequence rules (PMSR) from sequences of LBS cloaking regions generated from a series of LBS continuous queries. The main concept of the method is that it designs a probabilistic measurement of a support value of a sequence rule, and the implementation principle of the method is to iteratively achieve sequence rules. Finally, the authors conduct extensive experiments, and the results show that, compared to the non-probabilistic method, their proposed method has a significant matching ratio when the mined sequence rules are used as predictors, while the average accuracy of the sequence rules is comparable and computing performance is only slightly decreased.
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A Probabilistic Method For Mining Sequential Rules From Sequences Of Lbs Cloaking Regions

In this section, we present a probabilistic method for mining sequential rules (PMSR), which mines sequential rules from sequences of LBS cloaking regions generated from a set of LBS continuous queries. The method includes two key points: one is the definition of probabilistic measurement of a support value of a sequential rule, and the other is the strategy of iteratively achieving a sequential rule based on its length growth. First, we format the basic definitions of the PMSR method; then, we present the implementation algorithms of the PMSR method.

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