Fuzzy Weighted Clustering Method for Numerical Attributes of Communication Big Data Based on Cloud Computing

被引:7
作者
Ding, Haitao [1 ]
Sun, Chu [1 ]
Zeng, Jianqiu [1 ]
机构
[1] Beijing Univ Posts & Telecommun, Sch Econ & Management, Beijing 100876, Peoples R China
来源
SYMMETRY-BASEL | 2020年 / 12卷 / 04期
关键词
cloud computing; communication big data; numerical attributes; fuzzy weighting; clustering; IDENTIFICATION;
D O I
10.3390/sym12040530
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
It is necessary to optimize clustering processing of communication big data numerical attribute feature information in order to improve the ability of numerical attribute mining of communication big data, and thus a big data clustering algorithm based on cloud computing was proposed. The cloud extended distributed feature fitting method was used to process the numerical attribute linear programming of communication big data, and the mutual information feature quantity of communication big data numerical attribute was extracted. Combined with fuzzy C-means clustering and linear regression analysis, the statistical analysis of big data numerical attribute feature information was carried out, and the associated attribute sample set of communication big data numerical attribute cloud grid distribution was constructed. Cloud computing and adaptive quantitative recurrent classifiers were used for data classification, and block template matching and multi-sensor information fusion were combined to search the clustering center automatically to improve the convergence of clustering. The simulation results show that, after the application of this method, the information fusion performance of the clustering process was better, the automatic searching ability of the data clustering center was stronger, the frequency domain equalization control effect was good, the bit error rate was low, the energy consumption was small, and the ability of fuzzy weighted clustering retrieval of numerical attributes of communication big data was effectively improved.
引用
收藏
页数:14
相关论文
共 32 条
[1]  
Alfano S, 2018, ANN INFORM SYST, V21, P217, DOI 10.1007/978-3-319-58097-5_15
[2]   Vectors into the Future of Mass and Interpersonal Communication Research: Big Data, Social Media, and Computational Social Science [J].
Cappella, Joseph N. .
HUMAN COMMUNICATION RESEARCH, 2017, 43 (04) :545-558
[3]   Hybrid space-frequency domain pre-equalization for DC-biased optical orthogonal frequency division multiplexing based imaging multiple-input multiple-output visible light communication systems [J].
Chen, Chen ;
Zhong, Wen-De .
OPTICAL ENGINEERING, 2017, 56 (03)
[4]   Human behavior analysis in video surveillance: A Social Signal Processing perspective [J].
Cristani, Marco ;
Raghavendra, R. ;
Del Bue, Alessio ;
Murino, Vittorio .
NEUROCOMPUTING, 2013, 100 :86-97
[5]  
Devasena C.L., 2011, INT J COMPUT SCI ISS, V8, P635
[6]   Blind Identification of SM and Alamouti STBC-OFDM Signals [J].
Eldemerdash, Yahia A. ;
Dobre, Octavia A. ;
Liao, Bruce J. .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2015, 14 (02) :972-982
[7]   Parallel Monte Carlo Simulation of Single Polymer Chain [J].
Gao, He-Bei ;
Li, Hong ;
Qian, Chang-Ji .
INFORMATION TECHNOLOGY APPLICATIONS IN INDUSTRY, PTS 1-4, 2013, 263-266 :3317-+
[8]   Distance learning techniques for ontology similarity measuring and ontology mapping [J].
Gao, Wei ;
Farahani, Mohammad Reza ;
Aslam, Adnan ;
Hosamani, Sunilkumar .
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2017, 20 (02) :959-968
[9]  
Huang C.B., 2017, CHIN J LASERS, V44, P246
[10]  
Huang Zhanhua, 2017, Laser Technology, V41, P124, DOI 10.7510/jgjs.issn.1001-3806.2017.01.025