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- Title
HIGH EFFICIENT CLASSIFICATION MINING METHOD FOR REGIONAL LARGE DATA FEATURES UNDER COMPLEX ATTRIBUTE ENVIRONMENT.
- Authors
Jiali Li; Tao Cheng; Zhijie Zhao
- Abstract
In the process of large data mining in complex attribute environment, the classification effect of data features is poor, which affects the performance of high efficient classification and mining of large data features. In this paper, the authors analyze the high efficient classification mining method for regional large data features under complex attribute environment. The improved hybrid frog method is introduced to the classification, and a classification method based on improved hybrid frog is proposed in this study. Through the simulation test of Iris, Balance-scale and Ulass data sets, and compared with the classification performance of different intelligent classification method based on data mining method, it verified the effectiveness of the proposed method. The improved method needs to be further improved in performance. The other better improved hybrid frog method can be used to improve data classification and widely applied to various types of data mining.
- Subjects
DATA mining; CLASSIFICATION
- Publication
Academic Journal of Manufacturing Engineering, 2020, Vol 18, Issue 3, p113
- ISSN
1583-7904
- Publication type
Article