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Smart city article details
| Title | Towards Parallel Multi-Density Clustering For Urban Hotspots Detection |
|---|---|
| ID_Doc | 58249 |
| Authors | Cesario E.; Vinci A.; Zarin S. |
| Year | 2021 |
| Published | Proceedings - 29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2021 |
| DOI | http://dx.doi.org/10.1109/PDP52278.2021.00046 |
| 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. © 2021 IEEE. |
| Author Keywords | Multi density clustering; Smart City; Urban Computing |
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