Lightning fast asynchronous distributed K-means clustering

22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2014 - Proceedings

Volumn , Issue , 2014, Pages 99-104

  Berta, Árpád ^a   Hegedus, István ^a   Ormándi, Róbert ^a  

^a UNIVERSITY OF SZEGED   (Hungary)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA HANDLING; LEARNING SYSTEMS; NEURAL NETWORKS;

ASYNCHRONOUS NETWORKS; COMPUTATIONAL UNITS; DISTRIBUTED ARCHITECTURE; ENSEMBLE TECHNIQUES; FASTER CONVERGENCE; K-MEANS CLUSTERING; PROCESSING APPROACH; STATE OF THE ART;

DISTRIBUTED COMPUTER SYSTEMS;

EID: 84962022050     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. J. Dean and S. Ghemawat. Mapreduce: simplified data processing on large clusters. Commun. ACM, 51(1):107-113, 2008.
2. Y. Low, J. Gonzalez, A. Kyrola, D. Bickson, C. Guestrin, and J. M. Hellerstein. Graphlab: A new framework for parallel machine learning. arXiv:1006.4990, 2010.
3. R. Ormándi, I. Hegedu′s, and M. Jelasity. Gossip learning with linear models on fully distributed data. Concurrency and Computation: Practice and Experience, 25(4):556-571, 2012.
4. N. Tölgyesi and M. Jelasity. Adaptive peer sampling with newscast. In Euro-Par 2009, volume 5704 of LNCS, pages 523-534. Springer, 2009.
5. I. Eyal, I. Keidar, and R. Rom. Distributed data clustering in sensor networks. Distributed Computing, 24(5):207-222, 2011.
6. S. Bandyopadhyay, C. Giannella, U.Maulik, H. Kargupta, Kun Liu, and S. Datta. Clustering distributed data streams in peer-to-peer environments. Inf. Sci., 176(14):1952-1985, 2006.
7. G. Di Fatta, F. Blasa, S. Cafiero, and G. Fortino. Fault tolerant decentralised k-means clustering for asynchronous large-scale networks. J. Par. Distr. Comp., 73(3):317-329, 2013.
8. D. Kempe, A. Dobra, and J. Gehrke. Gossip-based computation of aggregate information. In Proc. 44th FOCS'03, pages 482-491. IEEE Computer Society, 2003.
9. Harold W. Kuhn. The hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2:83-97, 1955.
10. J. B. MacQueen. Some methods for classification and analysis of multivariate observations. In Proc. of 5th Berkeley Symposium on Math. Stat. and Prob., pages 281-297, 1967.
11. A. Frank and A. Asuncion. UCI machine learning repository, 2010.
12. C. D. Manning, P. Raghavan, and H. Schütze. Introduction to Information Retrieval. Cambridge University Press, 2008.
13. A. Montresor and M. Jelasity. Peersim: A scalable P2P simulator. In Proc. 9th IEEE Intl. Conf. on Peer-to-Peer Comp., pages 99-100. IEEE, 2009. extended abstract.
14. D. Stutzbach and R. Rejaie. Understanding churn in peer-to-peer networks. In Proc. 6th ACM Conf. on Internet measurement (IMC'06), pages 189-202. ACM, 2006.