提出了一种基于局部孤立系数(loc)的孤立点挖掘算法。
This paper presents a Local Outlier Coefficient - Based (LOC) Mining of Outliers.
这种方法使用了基于密度的孤立点挖掘的主要思想，用克隆选择算法进行数据立方体搜索。
It is a dense-based method and the low-dense data cubes are searched by clonal selection algorithm.
孤立点挖掘又称孤立点分析、异常检测、例外挖掘、小事件检测、挖掘极小类、偏差检测。
The problem of outlier mining has been variously called outlier analysis, anomaly detection, exception mining, detecting rare events, mining rare classes, deviation detection, etc.
该算法是对基于局部稀疏系数(LSC)孤立点挖掘论文中局部稀疏率和局部稀疏系数计算的一种改进。
This algorithm is an improvement of local sparsity ratio and local sparsity coefficient computation for local sparsity coefficient - Based (LSC) Mining of Outliers paper.
目前，在时间序列分析领域，孤立点的挖掘越来越多的受到重视。
At present, outlier mining has attached a great importance in the field of time series analysis.
孤立点分析是数据挖掘中的一个重要课题。
Analysis of outlier mining is one of the important problems in data mining.
为解决单个帖子线索的多话题性问题，识别聚类中的孤立点，提出一种基于模糊聚类的网络论坛（BBS）热点话题挖掘算法。
A bulletin board system(BBS) hot topic mining algorithm based on fuzzy clustering was developed to solve the problem of the post thread with multiple topics and identifying the outliers in clustering.
本文主要研究了基于孤立点因子的增量式挖掘技术。
We study the incremental data mining technology based outlier factor.
在模式挖掘方面，集成了目前有效的最大向前路径挖掘算法和频繁遍历路径挖掘算法，并且将孤立点分析方法引入日志挖掘中。
Efficient mining algorithm of maximum forward path and efficient mining algorithm of frequent traversal path are integrated in the mining period; Outlier analysis is introduced into the mining system.
在模式挖掘方面，集成了目前有效的最大向前路径挖掘算法和频繁遍历路径挖掘算法，并且将孤立点分析方法引入日志挖掘中。
Efficient mining algorithm of maximum forward path and efficient mining algorithm of frequent traversal path are integrated in the mining period; Outlier analysis is introduced into the mining system.
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