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1·How to use density kernel estimation for outlier detection?

如何使用密度核估计的孤立点检测？

2·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。

3·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。

4·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。

5·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。

6·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。

7·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。

8·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。

9·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。

10·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。