How to use density kernel estimation for outlier detection?
如何使用密度核估计的孤立点检测？
Outlier detection is a very important technique in data mining.
离群点发现是数据挖掘的一项重要技术。
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。
By using one of them, some robust multivariable outlier detection methods are built.
并利用其中一种，建立了稳健的多变量离群值的检测方法。
Abstract: spatial outlier detection is a research hotspot in the domain of spatial data mining.
摘要：空间离群模式探测是空间数据挖掘的一个研究热点。
The algorithm's complexity is low, it is suitable for quickly outlier detection of large data sets.
算法的复杂度较低，适用于大规模数据集快速离群点检测。
Based on above analysis, a reactive outlier detection approach over data stream has been introduced.
在此基础上，本文提出了一种响应式数据流异常检测方法。
This paper proposes a recursive forecast method for elevator traffic flow based on outlier detection.
提出一种基于异常值检测的电梯交通流递归预测方法。
An kernel clustering intrusion detection approach based on outlier detection is presented in this paper.
提出了一种基于孤立点检测的核聚类入侵检测方法。
This method also complements the shortages of current clustering algorithms in outlier detection and using.
此方法还弥补了现有聚类算法在离群点识别、使用上的缺欠。
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。
General approaches for outlier detection need to divide temporal data into sub-sequences so as to reduce complexity.
在对时序数据进行离群检测之前，一般先将原时序数据划分为若干个子序列，以便降低计算复杂度。
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。
Research on clustering analysis and outlier detection algorithms has become a highly active topic in the data mining research.
聚类及孤立点检测算法研究已经成为数据挖掘研究领域中非常活跃的一个研究课题。
Data Snooping or outlier detection is a main research subject in measurement data processing and measurement quality controlling.
粗差探测是测量数据处理、测量质量控制的重要研究主题之一。
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 addition, a new method of outlier detection in time series is proposed by combination of phase space theory and one-class classification method.
另外，结合一类分类方法和相空间重构理论，提出一种时间序列中的异常值检测方法。
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。
Aiming at the problem of large optimization size in dynamic outlier detection, this paper proposes a Kernel-based Real-time Outlier Detection (KROD) method.
针对动态野点数据检测过程中可能存在的优化规模过大的问题，提出了一种基于核方法的实时野点检测方法：KROD。
A method of outlier detection in regression is proposed making use of the character of structure risk function and KKT condition in support vector regression.
利用支持向量回归算法中结构风险函数较好的平滑性以及KKT条件，提出一种回归中的异常值检测方法。
A new method of outlier detection in time series is proposed in this paper, which is based on phase space reconstruction theory and one-class classification method.
论文结合相空间重构理论与一类分类方法提出一种时间序列中的异常值检测方法。
Outlier detection has special meaning in intrusion detection. So use replicator neural networks (RNNs) to provide an outlier detection method for intrusion detection.
孤立点检测在入侵检测中有着重要的意义，故将基于RNN的孤立点检测方法应用于网络入侵检测当中。
The accuracy of sensor data is a critical index to evaluate the performance of Wireless sensor Network (WSN). Outlier detection is a crucial but challenging issue for WSN.
数据的准确性是衡量无线传感器网络(wsn)性能的重要指标，异常数据检测是无线传感器网路面临的关键问题和主要挑战。
A method of outlier detection in re-gression is proposed making use of the character of structure risk function and KT condition in support vector regression in this paper.
该文利用支持向量回归算法中结构风险函数的性质以及KT条件，提出一种回归中的异常值检测方法。
By making use of the proximity query method in computational geometry, the whole matching query, pattern query, inverse query and outlier detection in time series are studied.
提出了计算几何应用到时间序列挖掘的方法，实现了时间序列全序列匹配查询、模式查询、反向查询和异常检测，查询效率和准确性都有了比较大的提高。
Secondly, we show the equivalence theorems between mean shift model and outlier detection model based on the condition that errors in regression model are normally distributed.
其次，在误差服从正态分布的条件下，阐述了线性回归模型框架中均值漂移模型与异常点检验的等价性。
The paper focuses on the sensitivity of local tangent space alignment (LTSA) to outliers, and presents a robust local tangent space alignment (RLTSA) based on outlier detection.
研究局部切空间排列方法(LTSA)对离群点的敏感性，提出一种基于离群点检测的鲁棒局部切空间排列方法(RLTSA)。
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。
We focus on finding abnormity in datasets with clustering and classified structure and studying the implement and optimization of key technology for outlier detection in this paper.
主要工作和成果如下：①对谱聚类基本原理和典型算法做了较为全面的分析和研究，利用谱聚类的特性实现了在复杂数据集上的聚类。
We focus on finding abnormity in datasets with clustering and classified structure and studying the implement and optimization of key technology for outlier detection in this paper.
主要工作和成果如下：①对谱聚类基本原理和典型算法做了较为全面的分析和研究，利用谱聚类的特性实现了在复杂数据集上的聚类。
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