Abstract
Because the traditional method is difficult to obtain the internal relationship and association rules of data when dealing with massive data, a fuzzy clustering method is proposed to analyze massive data. Firstly, the sample matrix was normalized through the normalization of sample data. Secondly, a fuzzy equivalence matrix was constructed by using fuzzy clustering method based on the normalization matrix, and then the fuzzy equivalence matrix was applied as the basis for dynamic clustering. Finally, a series of classifications were carried out on the mass data at the cut-set level successively and a dynamic cluster diagram was generated. The experimental results show that using data fuzzy clustering method can effectively identify association rules of data sets by multiple iterations of massive data, and the clustering process has short running time and good robustness. Therefore, it can be widely applied to the identification and classification of association rules of massive data such as sound, image and natural resources.
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10 September 2021
The originally published version of chapter 6 contained a few errors: the name of the third author was spelled wrong, the acknowledgment section was erroneously omitted. The name of the third author has been corrected as “Xiuming Li” and the acknowledgement section has been added.
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Acknowledgements
This work is supported by the Applied Basic Research Program of Qinghai (2019–ZJ–7017) Decision making and early warning of ecological animal husbandry development based on multimodal collaborative learning.
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Wu, H., Li, Q., Li, X. (2021). Research and Simulation of Mass Random Data Association Rules Based on Fuzzy Cluster Analysis. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1451. Springer, Singapore. https://doi.org/10.1007/978-981-16-5940-9_6
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DOI: https://doi.org/10.1007/978-981-16-5940-9_6
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