@inproceedings{Spezzano2018,
title = {A Nature-Inspired, Anytime and Parallel Algorithm for Data Stream Clustering},
author = {G. Spezzano and A. Vinci},
editor = {Sanzio Bassini, Marco Danelutto, Patrizio Dazzi, Gerhard R. Joubert, Frans Peters},
doi = {10.3233/978-1-61499-843-3-317},
issn = {1879808X},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {Parallel Computing is Everywhere. Preceedings of Parallel Computing Conference (ParCo2017)},
journal = {Advances in Parallel Computing},
volume = {32},
publisher = {IOS Press},
series = {Advances in Parallel Computing},
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}
}
