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

Haitao Zhang, Zewei Chen, Zhao Liu, Yunhong Zhu and Chenxue Wu
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Haitao Zhang: Nanjing University of Posts and Telecommunications, Nanjing, China
Zewei Chen: Nanjing University of Posts and Telecommunications, Nanjing, China
Zhao Liu: Nanjing University of Posts and Telecommunications, Nanjing, China
Yunhong Zhu: Nanjing University of Posts and Telecommunications, Nanjing, China
Chenxue Wu: Nanjing University of Posts and Telecommunications, Nanjing, China

International Journal of Data Warehousing and Mining (IJDWM), 2017, vol. 13, issue 1, 36-50

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.

Date: 2017
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