Name
Title | ||
|---|---|---|
A Probabilistic Method for Mining Sequential Rules from Sequences of LBS Cloaking Regions. |
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.4018/IJDWM.2017010102 | IJDWM |
Keywords | Field | DocType |
Data Mining, LBS Cloaking Regions, MultiRules, PMSR, Sequential Rules, SingleRules, Spatial-Temporal K-Anonymity | Data mining,Cloaking,Computer science,Probabilistic method,Artificial intelligence,Probabilistic logic,Anonymity,Data mining algorithm,Pound (mass),Machine learning | Journal |
Volume | Issue | ISSN |
13 | 1 | 1548-3924 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haitao Zhang | 1 | 24 | 8.76 |
| Zewei Chen | 2 | 0 | 0.34 |
| Zhao Liu | 3 | 25 | 10.73 |
| Yunhong Zhu | 4 | 0 | 0.68 |
| Chenxue Wu | 5 | 0 | 0.34 |