基于关联规则映射的多变量时间序列离群数据挖掘

Q4 Engineering
Yongjun Qin, Gihong Min
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引用次数: 1

摘要

在传统方法的异常数据挖掘中,由于数据复杂,异常数据没有得到有效的分类,增加了数据分类的复杂性,降低了数据挖掘的精度。本文提出了一种基于关联映射的时间序列异常数据挖掘方法。利用数据集之间的关联规则映射,确定数据集的关联规则。为了提高数据挖掘的精度,引入了挖掘因子和相对误差。采用混洗蛙跳聚类算法对挖掘因子进行聚类。基于聚类的多元时间序列分类用于基于时间序列训练集类别的聚类分类,结合改进的K近邻算法实现时间序列数据的分类和异常数据挖掘。实验结果表明,当数据集数量为200时,运行时间仅为12.9s。与传统方法相比,我们提出的方法可以有效地提高数据挖掘的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Outlier data mining of multivariate time series based on association rule mapping
In the outlier data mining with traditional methods, as the data is complex, the outlier data is not effectively classified, which increase the complexity of data classification and reduce the precision of data mining. In this paper, an outlier data mining method of time series based on association mapping is proposed. By using association rule mapping between datasets, the association rule of datasets is determined. The mining factor and relative error are introduced to improve the precision of data mining. The shuffled frog leaping clustering algorithm is applied to cluster the mining factor. The cluster-based multivariate time series classification is used for classification of clusters based on training set category of time series combined with modified K-nearest neighbour algorithm to achieve classification of time series data and outlier data mining. Experimental results show that running time is only 12.9 s when the number of datasets is 200. Compared with traditional methods, our proposed method can effectively improve the precision of data mining.
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来源期刊
International Journal of Internet Manufacturing and Services
International Journal of Internet Manufacturing and Services Engineering-Industrial and Manufacturing Engineering
CiteScore
0.70
自引率
0.00%
发文量
7
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