An outlier detection algorithm based on principal component analysis and the sum of attributes distance is proposed.
提出了一种基于主分量分析和属性距离和的孤立点检测算法。
According to these experiments, the proposed outlier detection algorithm for agricultural information can work well.
实验表明，本文提出的针对农产品价格数据的异常数据检测算法能够很好的完成任务。
According to the characteristics of spatial data sets, this paper proposes an outlier detection algorithm based on the Space Local Deviation Factor (SLDF).
针对空间数据集的特性，提出一种基于空间局部偏离因子(SLDF)的离群点检测算法。
To efficiently resolve outlier detection problem in large scale data sets, an efficient outlier detection algorithm based on Support Vector Data Description (SVDD) was proposed.
为了解决大规模数据中的异常检测问题，提出了基于支持向量数据描述（SVDD）的高效离群数据检测算法。
This paper presents an algorithm for outlier detection in distributed data streams.
针对分布式数据流环境，提出基于核密度估计的分布数据流离群点检测算法。
Experimental results show that the presented algorithm is effective in detecting outliers in the outlier subsequences detection and can improve the effectiveness of the outlier subsequences detection.
实验结果表明，所提出的算法对异常子序列的异常检测具有很好的效果，能够有效地提高时间序列中异常子序列检测的效率。
In order to enhance the robustness of LTSA algorithm, an outlier detection method based on the improved distance is presented in this paper.
基于编辑距离和多种后处理的生物医学文献实体名识别方法通过“全称缩写对识别算法”扩充词典，利用编辑距离算法提高识别召回率。
In order to enhance the robustness of LTSA algorithm, an outlier detection method based on the improved distance is presented in this paper.
基于编辑距离和多种后处理的生物医学文献实体名识别方法通过“全称缩写对识别算法”扩充词典，利用编辑距离算法提高识别召回率。
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