Abstract
In this paper, the fuzzy association algorithm based on Load Classifier is proposed to study the fuzzy association rules of numerical data flow. A method of dynamic partitioning of data blocks by load classifier for data stream is proposed, and the membership function of design optimization is proposed. The FP-Growth algorithm is used to realize the parallelization processing of fuzzy association rules. First, based on the load balancing classifier, variable window is proposed to divide the original data stream. Second, the continuous data preprocessing is performed and is converted into fuzzy interval data by the improved membership function. Finally through simulation experiments of the Load Classifier, compared with the four algorithms, the data processing time is similar after convergence, and the data processing time of SDBA (Spark Dynamic Block Adjustment Spark) is lower than 25 ms.
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Funding
This work was supported in part by the National Natural Science Foundation of P. R. China (No. 61672296, No. 61602261, and No. 61762071), the Major Natural Science Research Projects in Colleges and Universities of Jiangsu Province (No. 18KJA520008), and the Postgraduate Research and Practice Innovation Program of Jiangsu Province (No. SJKY19_0761, No. SJKY19_0759).
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Chen, J., Zheng, H., Li, P., Zhang, Z., Li, H., Liu, W. (2020). Fuzzy Association Rule Mining Algorithm Based on Load Classifier. In: He, J., et al. Data Science. ICDS 2019. Communications in Computer and Information Science, vol 1179. Springer, Singapore. https://doi.org/10.1007/978-981-15-2810-1_18
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