Discovering forward sequences from temporal data

Liu, Yu-Chin

doi:10.1016/j.knosys.2012.10.007

Cited by 1 publication

(1 citation statement)

References 33 publications

(48 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our context, an example of co-location is: within a radius of 200 m, mosquitoes nests are frequently found near ponds. On the contrary, other authors as Pei et al [19] or Liu [15] have studied temporal sequences which only take into account the temporal dimension. Tsoukatos et al [23] have extended these works to represent sets of environmental features evolving in time.…”

Section: Related Workmentioning

confidence: 99%

Spatio-sequential patterns mining: Beyond the boundaries

Alatrista-Salas

Bringay

Flouvat³

et al. 2016

IDA

View full text Add to dashboard Cite

International audienceData mining methods extract knowledge from huge amounts of data. Recently with the explosion of mobile technologies, a new type of data appeared. The resulting databases can be described as spatiotemporal data in which spatial information (e.g., the location of an event) and temporal information (e.g., the date of the event) are included. In this article, we focus on spatiotemporal patterns extraction from this kind of databases. These patterns can be considered as sequences representing changes of events localized in areas and its near surrounding over time. Two algorithms are proposed to tackle this problem: the first one uses \emph{a priori} strategy and the second one is based on pattern-growth approach. We have applied our generic method on two different real datasets related to: 1) pollution of rivers in France; and 2) monitoring of dengue epidemics in New Caledonia. Additionally, experiments on synthetic data have been conducted to measure the performance of the proposed algorithms

show abstract

Section: Related Workmentioning

confidence: 99%