Paper Info
Title
Modeling Dynamic Spatial-Temporal Cluster Relationships
Abstract
Spatial-temporal data refers to potentially massive amounts of data gathered across both space and time. Spatial-temporal data analysis helps uncover the value that this type of data holds to domains such as transportation operations, traffic management, service demand, and trip planning. Specifically, cluster analysis groups data into sets known as clusters such that elements inside a cluster are more similar to each other than elements in other dusters. Cluster analysis has been successfully applied in domains such as transportation, ecology, medicine, and astronomy. However, current duster analysis techniques limit themselves to static cluster analysis, thereby missing the identification of interesting insights and patterns related to the evolution of clusters over time. In this paper, we clarify the concept of dynamic clusters and support new forms of duster analyses by introducing, describing, and formalizing duster relationships that represent important events, such as split or merge, that a cluster may go through from its start to its end. These relationships provide a foundation for investigating cluster evolution and providing novel insights for better operational and business decision making.
Year
DOI
Venue
2019
10.1109/BigData47090.2019.9006496
2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
Keywords
Field
DocType
Cluster analysis, spatial-temporal data, spatial-temporal relationships
Data mining,Cluster (physics),Trip planning,Computer science,Business decision mapping,Service demand,Merge (version control)
Conference
ISSN
Citations 
PageRank 
2639-1589
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Ivens Portugal1291.99
Paulo S. C. Alencar239345.89
Donald Cowan328415.65