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In the bounded linear operator approximation theory of Hilbert Space,the isolate point of operator essential spectrum is always a problem. In this paper,a general method to treat this problem is given.

孙辉在Hilbert空间线性算子逼近论中,算子本质谱的孤立点通常是处理一些问题的主要障碍之一,本文给出处理算子本质谱的孤立点的一般方法。

In this method, the outlier's density is considered smaller than the average density of data set and the none-outlier's density shouldn't decrease with its closed interval compression.

该方法提出了孤立点对象的密度要小于数据集的平均密度;非孤立点对象的密度不应因为封闭区间的收缩而减少。

An isolated point ' s statistic excluding method is proposed in this paper to eliminate crassitude error in clouding data , which include plenty of oddity data .

针对大量含奇异点的数据点云,本文提出了剔除粗大误差的孤立点统计排异法,该方法根据对相邻点距离的统计,剔除在正态分布以外的点

An isolated point s statistic excluding method is proposed in this paper to eliminate crassitude error in clouding data, which include plenty of oddity data. The method based on the distance between two neighbour points can eliminate the data beyond normal distribution. A error limitation of angle and chordal highness method is used to filtrate clouding point.

针对大量含奇异点的数据点云,本文提出了剔除粗大误差的孤立点统计排异法,该方法根据对相邻点距离的统计,剔除在正态分布以外的点;对大量数据的精减,利用角度和弦高的最大允许偏差法进行点云精减。

Example analysis makes clear, this algorithm can detect effectively the isolated dot in data set, the isolated dot that digs disinter accords with actual condition.

实例分析表明,该算法能有效检测数据集中的孤立点,挖掘出的孤立点符合实际情况。

Analyze the isolated point with the query tools.

对于数据挖掘中发现的孤立点,结合论坛运行实际情况及查询工具,分析孤立点产生原因。

This model adopts valuation indexes that extracted from transaction records as supplementations to the RFM parameters, and the algorithm for isolated point detection based on similar coefficient sum is also used to reduce the affection of isolated point.

该模型从客户交易信息中选取评估指标作为RFM指标的补充,并应用基于相似系数和的孤立点检测法降低孤立点对聚类结果的干扰。

The method includes the following steps: firstly, a spherical neighborhood of present sample points is established by using the geometric spherical-modelling theory and all the sample points contained in the spherical neighborhood are adopted as candidate neighbor points, thus not only preserving the effectivity of the dimension reduction capability when data sets are sparse but also getting the advantages of low-sensitivity to isolated points and good stability of the preserved topological structure; then a data relevance matrix more matching semantics can be obtained by relevance measurement based on route clusters to update the candidate neighbor points in the spherical neighborhood and optimize the regular neighborhood space of the present sample points, thus improving the phenomenon that the dimension reduction of sample sets provided with folded curved faces is apt to suffer the integrated-structure distortion in case of heterogeneous data distribution.

首先利用几何开球原理建立当前样本点的球状邻域,将包含在球状邻域内的所有样本点作为候选近邻点,不但能够保持在数据集稀疏情况下的降维性能的有效性,而且具有对孤立点敏感性不高、保留拓扑结构稳定性好的优点。然后利用基于路径聚类的相关性度量得到更符合语义的数据相关性矩阵,用来对球状邻域内的候选近邻点进行更新,优化当前样本点的规则邻域空间,改善了当数据不均匀分布时在带有折叠弯曲面的样本集上降维容易出现整体结构扭曲的现象。

Propose a new agglomerative hierarchical clustering based method to eliminate outliers,with clustering tree to identify outliers.

提出一种基于凝聚层次聚类消除孤立点的新方法,借助聚类树识别孤立点

For contrast experiments, k-means and DBSCAN algorithms are also implemented using Visual C++ 6. 0. We conducted a series of experiments, including the experiment of the correctness of clustering and outlier detection, the experiment of the precision of clustering and outlier detection. The experiment of the runtime, the experiment of the effect of clustering and outlier detection on parameters, the experiment of the impact of clustering and outlier detection precision by the order of data input, and the experiment of the effect of the algorithm validness by the density character of dataset.

本文使用Visual C++ 6.0实现了基于距离的聚类和孤立点检测算法、k-means算法和DBSCAN算法,做了大量的对比实验,包括聚类算法和孤立点检测正确性实验;聚类算法和孤立点检测精度实验;算法执行时间实验;参数对聚类和孤立点检测结果的影响实验;数据输入顺序对算法聚类和孤立点精度的影响实验;数据集密度对算法有效性的影响等。

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