Abstract
A dynamic intelligence expression method is presented in this paper, which uses big data analysis to represent the intelligence to be taken from Web. In this method, reasoning methods are used to create new ideas which can be added to field intelligence systems in favor of big data analysis. This is used for the generalization of the well-known analysis to implement rule based generalization. The method plans to produce a learning model which best take offs the class members of a marked rule base. The object categories are given by an interface which is represented by the standards of a mathematical method. The category is defined by the formula. In our big data method, the learned artificial intelligence model is represented by models and it is consisted of a best condition of expressions of a given category. We show that this feature gives scholar choices to get ideas into the application field. Furthermore, the expression according to models adds additional value to the function and enables to answer questions, which big data function method cannot. The big data expression of the models can be explained by scholar. The reasoning logic can be added to the existing artificial intelligence expression method. Additionally, the reasoning logic obtaining method can be used repeatedly. In each procedure, new ideas from the search step can be added to the reasoning rule sets to enhance the comprehensive characteristics of the presented reasoning methods.



Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Change history
30 November 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s10586-022-03829-3
References
Kelkar, A., Manwade, K.B.: Identifying nearly fusion records in relational internet of thing. Int. J. Comput. Sci. Inf. Technol. Sec. 2, 514–517 (2012)
Hu, R.: Channel access controlling in wireless integrated information network using smart grid system. Appl. Math. Inf. Sci. 6, 813–820 (2012)
Kishore, J.K., Patnaik, L.M., Mani, V., Agrawal, V.K.: Application of genetic programming for multi-category pattern generalization. IEEE. Trans. Evol. Comput. 4(3), 242–258 (2014). https://doi.org/10.1109/4235.873235
McCallum, A., Nigam, K., Ungar, L.H.: Efficient clustering of high-dimensional data sets with application to reference matching. In: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data mining, Boston, 20–23 August 2000, pp. 169–178 (2000)
Hu, R., Jiang, C.Y., Xu, H.: A new efficiency judging method for healthy big data system using heuristic algorithm. Basic Clin. Pharmacol. Toxicol. 118, 42 (2016)
Aizawa, A., Oyama, K.: A fast linkage matching scheme for multi-source information integration. In: Proceedings of the International Workshop on Challenges in Web Information Retrieval and Integration, Tokyo, 8–9 April 2005, pp. 30–39. (2005). https://doi.org/10.1109/WIRI.2005.2
Ruo, H.: New network access control method using intelligence agent technology. Appl. Math. Inf. Sci. 7, 44–48 (2013)
Metha, K., Alnoory, M., Aqel, M.: Performance evaluation of similarity functions for fusion record matching. Master’s Thesis, Middle East University, Beirut (2011). http://elibrary.mediu.edu.my/books/2014/MEDIU11238.pdf
Hu, R., Hu, H., Xu, H.: Abnormal access matching through big data analytics in health neural network. Basic Clin. Pharmacol. Toxicol. 118, 73 (2016)
Christen, P.: Performance and scalability of fast blocking methods for removing semantic collision and data linkage. In: Proceedings of the VLDB Endowment, Vienna, 23–28 September, vol. 1, pp. 1253–1264 (2007)
Raghavan, H., Allan, J.: Using matching method codes for indexing names in ASR documents. In: Proceeding SpeechIR ‘04 Proceedings of the Workshop on Inter-disciplinary Approaches to Speech Indexing and Retrieval at HLT-NAACL 2004, Boston, 6 May 2004, pp. 22–27 (2004). https://doi.org/10.3115/1626307.1626312
Hu, R., Hu, H., Xiao, Z.H.: Matching unit of health neural network unit based on relation object framework. Basic Clin. Pharmacol. Toxicol. 118, 72–73 (2016)
Agrawal, G.K., Berg, D.: Technology in the system study process: a missing dimension. Int. J. Syst. Technol. Manag. 8(2–3), 107–122 (2007)
Robinson, D.: Implications of neural networks for how we think about brain function. Behav. Brain. Sci. 15, 644–655 (2012)
Acknowledgements
This study is supported by Natural Science Fund Project in Guangdong province (No. 2015A030313671) and Major Project for Guangzhou Collaborative Innovation of Industry-University-Research (No. 201704020196). This study is supported by Guangzhou Key Laboratory of Digital Content Processing and Security Technologies and Guangdong Provincial Application-oriented Technical Research and Development Special fund project (2016B010127006) and International Scientific and Technological Cooperation Projects of Guangdong Province (2017A050501039).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Hu, R., Zhao, Hm. & Xu, H. RETRACTED ARTICLE: A big data intelligence analysis expression method based on machine learning. Cluster Comput 22 (Suppl 4), 8017–8024 (2019). https://doi.org/10.1007/s10586-017-1578-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-017-1578-9