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Research on time series data mining algorithm based on Bayesian node incremental decision tree

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Abstract

Aiming at the shortage of classic ID3 decision tree and C4.5 decision tree algorithm in ability of time series data mining, this paper increases Bayesian classification algorithm in the node of the decision tree algorithm as a preprocessing Bayesian node to form incremental learning decision tree. In incremental learning, the decision tree is more powerful in the mining of time series data, and the result is more robust and accurate. In order to verify the effectiveness of the algorithm, time series data mining simulation based on UCR time series data sets have been conducted, and the performance and efficiency of ID3 decision tree, Bayesian algorithm and incremental decision tree algorithm have been contrasted respectively in non-incremental learning and incremental learning. The experimental results show that in both the incremental and non-incremental time series data mining, the incremental decision tree algorithm based on Bayesian nodes optimization which can improve the classification accuracy. When optimizing the parameters of Bayesian nodes, the consumption of pruning time will be decreased, and the efficiency of the data mining is greatly improved.

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  1. Huanggang Normal University, Huanggang, Hubei Province, China

    Sun Xingrong

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  1. Sun Xingrong
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Xingrong, S. Research on time series data mining algorithm based on Bayesian node incremental decision tree. Cluster Comput 22 (Suppl 4), 10361–10370 (2019). https://doi.org/10.1007/s10586-017-1358-6

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