Data Mining Algorithm Based on Fusion Computer Artificial Intelligence Technology

Yingqian Bai; Kepeng Bao; Tao Xu

doi:10.4108/eetsis.3779

Authors

Yingqian Bai ShaanXi Railway Institute
Kepeng Bao Xi'an Polytechnic University
Tao Xu Xi'an Polytechnic University

DOI:

https://doi.org/10.4108/eetsis.3779

Keywords:

Artificial intelligence, Data mining, Information entropy, Data schema tree, Neural network

Abstract

INTRODUCTION: The paper constructs a massive data mining model of distributed spatiotemporal databases for the Internet of Things. Then a homologous data fusion method based on information entropy is proposed. The storage space required by the tree structure is reduced by constructing the data schema tree of the merged data set. Secondly, the optimal dynamic support degree is obtained by using a neural network and genetic algorithm. Frequent items in the Internet of Things data are mined to achieve the normalization of the clustered feature data based on the threshold value. Experiments show that the F-measure of the data mining algorithm improves the efficiency by 15.64% and 18.25% compared with the kinds of other literatures respectively. RI increased by 21.17% and 26.07%, respectively.

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