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基于粗糙神经网络的低维冗余非结构化数据快速挖掘算法

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为了更好地优化挖掘效率、减小挖掘误差、减少挖掘响应时间,根据非结构化数据特点,在粗糙集策略的基础上,利用粗糙神经网络对低维冗余非结构化数据进行挖掘过程优化.建立基于粗糙神经网络的非结构化数据清洗模型,提取低维冗余非结构化数据特征,挖掘支持量计算并输出.实验结果表明,经过所提算法优化后的挖掘效果,在整体效率、响应速度和挖掘精度上均表现出色,能够满足实际应用要求,解决了误差偏大、效率偏低问题.
Rapid Mining Algorithm for Low-dimensional Redundant Uunstructured Data Based on Rough Neural Network
In order to better optimize the mining efficiency,reduce the mining error and the mining response time,according to the characteristics of unstructured data,on the basis of rough set strategy,the rough neural network is used to optimize the mining process of low-dimensional redundant unstructured data.The unstructured data cleaning model is established based on rough neural network.Low-dimensional redundant unstructured data feature is extracted.Mining support quantity is calculated and outputted.The experimental results show that the mining effect optimized by the proposed method is excellent in terms of overall efficiency,response speed,and mining accuracy.It can meet the requirements of practical applications,and solve the problems of large error and low efficiency.

rough neural networklow-dimensional redundancyunstructuredrapid mining

张晓荣、薛鹏程、李岩

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国网甘肃省电力公司天水供电公司,办公室,甘肃,天水 741000

国网甘肃省电力公司,办公室,甘肃,天水 740000

粗糙神经网络 低维冗余 非结构化 快速挖掘

2024

微型电脑应用
上海市微型电脑应用学会

微型电脑应用

CSTPCD
影响因子:0.359
ISSN:1007-757X
年,卷(期):2024.40(12)