Online incremental updating for model enhancement based on multi-perspective trusted intervals

Fang, Na; Fang, Xianwen; Lu, Ke

doi:10.1080/09540091.2022.2088696

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“…A minimum is removed for each vector dimension to ensure that the vector dimensions are between 0 and 1. The algorithm divides the obtained result with the maximum value of each dimension so that the data of each dimension can be normalized [14]. The experimental data were sampled at random without repetition.…”

Section: Acquisition Of Experimental Data Setsmentioning

confidence: 99%

Data Mining Algorithm Based on Fusion Computer Artificial Intelligence Technology

Bai,

Bao,

2023

ICST Transactions on Scalable Information Systems

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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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Section: Acquisition Of Experimental Data Setsmentioning

confidence: 99%