2019
DOI: 10.1007/978-3-030-30244-3_34
|View full text |Cite
|
Sign up to set email alerts
|

Discovering Common Pathways Across Users’ Habits in Mobility Data

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
2

Citation Types

0
9
0

Year Published

2020
2020
2023
2023

Publication Types

Select...
2
2

Relationship

1
3

Authors

Journals

citations
Cited by 4 publications
(9 citation statements)
references
References 19 publications
0
9
0
Order By: Relevance
“…This study extends the work originally presented in the EPIA Conference on Artificial Intelligence (Andrade et al, 2019a) including more experiments for the proposed techniques over two different mobility datasets (one new dataset added from previous work).…”
Section: Introductionmentioning
confidence: 53%
See 3 more Smart Citations
“…This study extends the work originally presented in the EPIA Conference on Artificial Intelligence (Andrade et al, 2019a) including more experiments for the proposed techniques over two different mobility datasets (one new dataset added from previous work).…”
Section: Introductionmentioning
confidence: 53%
“…This dataset was added to this extended version of the paper Discovering Common Pathways Across Users' Habits in Mobility Data (Andrade et al, 2019a). For illustrative purposes, we are going to use the personal data acquired from Google Location Services.…”
Section: Methodsmentioning
confidence: 99%
See 2 more Smart Citations
“…For example, in [28] the classical K-means clustering algorithm to identify locations of interest was used. The DBSCAN (density-based spatial clustering of applications with noise) algorithm and different derivative algorithms of the DBSCAN have been adopted in many studies in order to identify stop locations [20,15,2,12]. These density-based clustering algorithms offer some advantages comparing with the K-means approach including the capacity of identifying clusters of varying shapes [15].…”
Section: Introductionmentioning
confidence: 99%