The Analysis of Non-Significant Feature Data Mining in Big Data Environments

Authors

  • Xiaoli Meng Department of Engineering, Xi’an International University, Xi’an 710077, China

Abstract

In order to cope with the problem of low precision in data mining, it is necessary to study the non-significant features of data mining methods. The current method shows efficiency bias in the data mining. In this paper, a non-significant feature data mining method based on Ant Colony Clustering is proposed. This method extracts the characteristics of data clustering which manifest the significant characteristics of data mining in a big data environment. Experiments show that this method is more accurate when data mining.

Keywords: Big data environment; Non-significant feature; Data mining; Ant Colony Clustering algorithm.

Cite As

X. Meng, “The Analysis of Non-Significant Feature Data Mining in Big Data Environmentsâ€,
Engineering Intelligent Systems, vol. 28 no. 1, pp. 41-49, 2020.


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Published

2020-03-01

Issue

Vol. 28 No. 1 (2020): International Journal of Engineering Intelligent Systems

Section

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