A Data Mining Algorithm based on Relevant Vector Machine of Cloud Simulation

doi:10.23940/ijpe.18.06.p28.13601364

Abstract

Abstract:

Regarding the problems of long time running and memory overflowing caused from the analysis of data mining algorithms for tactical communication network simulation data, using relevance vector machine (RVM), a data mining algorithm that is mainly used on the small sample of data mining with a good effect but a large amount of calculation that is based on an open source distributed storage and computing platform Hadoop, the author designs a kind of relevance vector machine data mining algorithm based on cloud computing. Based on the sum of the distribution of small sample data mining law in sequence, in some cases, the algorithm reflects the law of large sample data mining. Then, it carries on programming and empirical research, which supports the analysis of massive cloud simulation data.

Submitted on March 29, 2018; Revised on April 12, 2018; Accepted on May 23, 2018
References: 14

Wuqi Gao, Gang Li, and Hui Liu. A Data Mining Algorithm based on Relevant Vector Machine of Cloud Simulation [J]. Int J Performability Eng, 2018, 14(6): 1360-1364.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References 0

	U. Aich, S.Banerjee, “Modeling of EDM responses by support vector machine regression with parameters selected by particle swarm optimization”, Applied Mathematical Modelling, vol. 38, no(11-12), pp.2800-2818, 2014.
	A. W. Blocker, F. V. Bonassi, S. L. Scott, et al., “Bayes and big data: The consensus Monte Carlo algorithm”, International Journal of Management Science and Engineering Management, vol.11, no 2, pp. 78-88, 2016.
	Y. Chaudhary, J. Joshi, S. Porwal, et al., “Data compression methodologies for lossless data and comparison between algorithms”, International Journal of Engineering Science and Innovative Technology (IJESIT), vol. 2, no 2, pp. 142-147, 2013.
	S. Decherchi, P. Gastaldo, A. Leoncini, et al., “Efficient digital implementation of extreme learning machines for classification”, IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 59, no 8, pp. 496-500, 2013.
	W. Ding, H. Lo, D. Wang, et al., “Crime hotspot mapping using the crime related factors—a spatial data mining approach”, Applied intelligence, vol. 39, no. 4, pp.772-781, 2013.
	B. L. Evans, J. Lin, M. Nassar, “Impulsive noise mitigation in powerline communications using sparse Bayesian learning”, IEEE Journal on Selected Areas in Communications, vol. 31, no. 7, pp.1172-1183, 2013.
	M. Fazel, A. Jalali, S. Oymak, et al., “Simultaneously structured models with application to sparse and low-rank matrices”, IEEE Transactions on Information Theory, vol.61, no.5, pp. 2886-2908, 2015.
	L. Guo, C. Ruan, M. Wang, et al., “A cloud simulation based environment for multi-disciplinary collaborative simulation and optimization”, in International Conference on Computer Supported Cooperative Work in Design, pp 445-450, 2017
	H. Gupta, S. K. Ghosh, A. Vahid Dastjerdi, et al., “iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments”, Software: Practice and Experience, vol. 47, no.9, pp1275-1296, 2017.
	J. Huang, J. W. Park, S. Shen, et al., “MATS: a Bayesian framework for flexible detection of differential alternative splicing from RNA-Seq data”, Nucleic Acids Research, vol.40, no 8, pp. e61-e61, 2012.
	F. Kang, J. Li, “Artificial bee colony algorithm optimized support vector regression for system reliability analysis of slopes”, Journal of Computing in Civil Engineering, vol. 30, no. 3, 04015040, 2015
	D. Liu, D. Y. Peng, Pan, X. Peng & J. Zhou, “Lithium-ion battery remaining useful life estimation with an optimized Relevance Vector Machine algorithm with incremental learning”, Measurement, vol. 63, pp143-151, 2015
	P. Wendell, R. S. Xin, M. Zaharia, et al. Apache spark: a unified engine for big data processing”, Communications of the ACM, vol. 59, no.11, pp56-65, 2016
	X. Wu, G. Q. Wu, X. Zhu, & W. Ding, “Data mining with big data”, IEEE transactions on Knowledge and Data Engineering, vol. 26, no. 1, pp. 97-107,2014.