Optimization of Intelligent Data Mining Technology in Big Data Environment

Wei Wang

doi:10.20965/jaciii.2019.p0129

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JACIII Vol.23 No.1 pp. 129-133

doi: 10.20965/jaciii.2019.p0129

(2019)

Short Paper:

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Optimization of Intelligent Data Mining Technology in Big Data Environment

Wei Wang

Department of Information Engineering, Henan Industry and Trade Vocational College
Zhengzhou, Henan 451191, China

Received:

May 29, 2018

Accepted:

July 6, 2018

Published:

January 20, 2019

Keywords:

massive data, big data environment association rules, data mining technology

Abstract

At present, storage technology cannot save data completely. Therefore, in such a big data environment, data mining technology needs to be optimized for intelligent data. Firstly, in the face of massive intelligent data, the potential relationship between data items in the database is firstly described by association rules. The data items are measured by support degree and confidence level, and the data set with minimum support is found. At the same time, strong association rules are obtained according to the given confidence level of users. Secondly, in order to effectively improve the scanning speed of data items, an optimized association data mining technology based on hash technology and optimized transaction compression technology is proposed. A hash function is used to count the item set in the set of waiting options, and the count is less than its support, then the pruning is done, and then the object compression technique is used to delete the item and the transaction which is unrelated to the item set, so as to improve the processing efficiency of the association rules. Experiments show that the optimized data mining technology can significantly improve the efficiency of obtaining valuable intelligent data.

Cite this article as:

W. Wang, “Optimization of Intelligent Data Mining Technology in Big Data Environment,” J. Adv. Comput. Intell. Intell. Inform., Vol.23 No.1, pp. 129-133, 2019.

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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] I. Makarova, R. Khabibullin, V. Mavrin, et al., “Application of data mining technology to optimize the city transport network,” Int. Conf. on Application of Information and Communication Technologies, pp. 1-5, 2016.

[2] [2] G. Yang, W. Tan, H. Jin, et al., “Review wearable sensing system for gait recognition,” Cluster Computing, pp. 1-9, 2018.

[3] [3] P. Xue, Z. Zhou, X. Fang, et al., “Fault detection and operation optimization in district heating substations based on data mining techniques,” Applied Energy, Vol.205, No.3, pp. 926-940, 2017.

[4] [4] Z. Feng, X. Li, Q. Zhang, et al., “Proactive Radio Resource Optimization with Margin Prediction: A Data Mining Approach,” IEEE Trans. on Vehicular Technology, Vol.66, Issue 10, pp. 9050-9060, 2017.

[5] [5] G. Yang, X. Deng, and C. Liu, “Facial expression recognition model based on deep spatiotemporal convolutional neural networks,” J. of Central South University (Science and Technology), Vol.47, No.7, pp. 2311-2319, 2016.

[6] [6] Y. Peng, X. Yang, and W. Xu, “Optimization Research of Decision Support System Based on Data Mining Algorithm,” Wireless Personal Communications, Vol.4, pp. 1-13, 2018.

[7] [7] H. Q. Li and J. J. Wang, “Data Stream Association Rule Mining Algorithm in Hybrid Cloud Environment,” Microelectronics & Computer, Vol.33, No.11, pp. 152-156, 2016.