2021
Cesario, E.; Vinci, A.; Zarin, S.
Towards Parallel Multi-density Clustering for Urban Hotspots Detection Proceedings Article
In: 2021 29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), IEEE, 2021, ISBN: 9781665414555.
Abstract | Links | BibTeX | Tag: Multi density clustering, smart city, Urban computing
@inproceedings{Cesario2021,
title = {Towards Parallel Multi-density Clustering for Urban Hotspots Detection},
author = {E. Cesario and A. Vinci and S. Zarin},
doi = {10.1109/PDP52278.2021.00046},
isbn = {9781665414555},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {2021 29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)},
journal = {Proceedings - 29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2021},
publisher = {IEEE},
abstract = {Detecting city hotspots in urban environments is a valuable organization methodology for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial urban datasets. Such knowledge is a valuable support for planner, scientist and policy-maker's decisions. Classic density-based clustering algorithms show to be suitable to discover hotspots characterized by homogeneous density, but their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering approaches show higher effectiveness to discover city hotspots. Moreover, the growing volumes of data collected in urban environments require high-performance computing solutions, to guarantee efficient, scalable and elastic task executions. This paper describes the design and implementation of a parallel multi-density clustering algorithm, aimed at analyzing high volume of urban data in an efficient way. The experimental evaluation shows that the proposed parallel clustering approach takes out encouraging advantages in terms of execution time and speedup.},
keywords = {Multi density clustering, smart city, Urban computing},
pubstate = {published},
tppubtype = {inproceedings}
}
Detecting city hotspots in urban environments is a valuable organization methodology for framing detailed knowledge of a metropolitan area, providing high-level summaries for spatial urban datasets. Such knowledge is a valuable support for planner, scientist and policy-maker's decisions. Classic density-based clustering algorithms show to be suitable to discover hotspots characterized by homogeneous density, but their application on multi-density data can produce inaccurate results. For such a reason, since metropolitan cities are heavily characterized by variable densities, multi-density clustering approaches show higher effectiveness to discover city hotspots. Moreover, the growing volumes of data collected in urban environments require high-performance computing solutions, to guarantee efficient, scalable and elastic task executions. This paper describes the design and implementation of a parallel multi-density clustering algorithm, aimed at analyzing high volume of urban data in an efficient way. The experimental evaluation shows that the proposed parallel clustering approach takes out encouraging advantages in terms of execution time and speedup.