Spezzano, Giandomenico; Vinci, Andrea A nature-inspired, anytime and parallel algorithm for data stream clustering Inproceedings Parallel Computing is Everywhere, Proceedings of the International Conference on Parallel Computing, ParCo 2017, 12-15 September 2017, Bologna, Italy, pp. 317–326, IOS Press, Amsterdam, 2017, ISBN: 978-1-61499-842-6. Abstract | Links | BibTeX | Tags: Anytime Algorithm, Clustering, CUDA, Data Stream, general purpose GPU computing @inproceedings{CNRPRODOTTI378141,
title = {A nature-inspired, anytime and parallel algorithm for data stream clustering},
author = {Giandomenico Spezzano and Andrea Vinci},
url = {https://doi.org/10.3233/978-1-61499-843-3-317},
doi = {10.3233/978-1-61499-843-3-317},
isbn = {978-1-61499-842-6},
year = {2017},
date = {2017-01-01},
booktitle = {Parallel Computing is Everywhere, Proceedings of the International Conference on Parallel Computing, ParCo 2017, 12-15 September 2017, Bologna, Italy},
pages = {317--326},
publisher = {IOS Press},
address = {Amsterdam},
series = {Advances in parallel computing (Online)},
abstract = {In the context of time-critical applications there exists the need of clustering data streams so as to provide approximated solutions in the shortest possible time, in order to capture in real-time the evolution of physical or social phenomena. In this work, a nature-inspired algorithm for clustering of evolving big data stream is presented, which is designed to be executed on many-core GPU architectures.},
keywords = {Anytime Algorithm, Clustering, CUDA, Data Stream, general purpose GPU computing},
pubstate = {published},
tppubtype = {inproceedings}
}
In the context of time-critical applications there exists the need of clustering data streams so as to provide approximated solutions in the shortest possible time, in order to capture in real-time the evolution of physical or social phenomena. In this work, a nature-inspired algorithm for clustering of evolving big data stream is presented, which is designed to be executed on many-core GPU architectures. |
Spezzano, Giandomenico; Vinci, Andrea Realizzazione di un algoritmo bio-ispirato per il clustering di stream di dati evolventi su architettura GPU Technical Report 2015. BibTeX | Tags: data stream mining, general purpose GPU computing, swarm intelligence @techreport{CNRPRODOTTI342302,
title = {Realizzazione di un algoritmo bio-ispirato per il clustering di stream di dati evolventi su architettura GPU},
author = {Giandomenico Spezzano and Andrea Vinci},
year = {2015},
date = {2015-01-01},
keywords = {data stream mining, general purpose GPU computing, swarm intelligence},
pubstate = {published},
tppubtype = {techreport}
}
|
