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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 11
3274-3277,3333

Network big data classification processing method based on Spark and distributed KNN classifier

Cao Yu1
Wang Nan2,3
Xu Zhichao2
1. Dept. of Computer, Harbin Finance University, Harbin 150030, China
2. Institute of Management & Credit, Jilin University of Finance & Economics, Changchun 130117, China
3. School of Computing, Jilin University, Changchun 130012, China
DOI: 10.19734/j.issn.1001-3695.2018.05.0296

Abstract

Aiming at the limitation that the existing big data classification methods cannot meet the time and storage space in big data applications, this paper proposed a design method of big data parallel multi-label K-nearest neighbor classifier based on Apache Spark framework. In order to reduce the cost of the existing MapReduce scheme by using other memory operations, firstly, it divided the training set into several partitions in conjunction with the parallel mechanism of the Apache Spark framework. Then in the map stage, it found the K-nearest neighbors of each partition of the sample to be predicted, and in the reduce phase, it determined the final K-nearest neighbors according to the results of the map phase. Finally, it aggregated the neighboring tag sets in parallel, and output the target tag set of the sample to be predicted by maximizing the posterior probability. Experiments were carried out on four big data classification datasets such as PokerHand. The proposed method achieves a lower Hamming loss and proves its effectiveness.

Foundation Support

国家自然科学基金资助项目(61702213)
吉林省教育厅“十三五”科学技术研究(JJKH20180463KJ)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.05.0296
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 11
Section: Algorithm Research & Explore
Pages: 3274-3277,3333
Serial Number: 1001-3695(2019)11-016-3274-04

Publish History

[2019-11-05] Printed Article

Cite This Article

曹瑜, 王楠, 徐志超. Spark框架结合分布式KNN分类器的网络大数据分类处理方法 [J]. 计算机应用研究, 2019, 36 (11): 3274-3277,3333. (Cao Yu, Wang Nan, Xu Zhichao. Network big data classification processing method based on Spark and distributed KNN classifier [J]. Application Research of Computers, 2019, 36 (11): 3274-3277,3333. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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