基于粗糙聚类的局部离群点发现

2011 3rd International Workshop on Intelligent Systems and Applications Pub Date : 2011-05-28 DOI:10.1109/ISA.2011.5873272

Hongjuan Mi

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引用次数: 3

摘要

数据点上的密度是基于核函数定义的。并引入权值来改进粗糙k-means算法。然后基于改进的粗糙k-means算法生成的聚类，构造局部离群值的计算公式。我们使用合成数据集和实际数据集验证了局部异常点检测的新技术不仅准确而且高效。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Discovering Local Outlier Based on Rough Clustering

The density at a data point is defined based on kernel function. And we introduce weight to refine rough k-means algorithm. Then we construct the formula for calculating local outlier score based on the clusters generated by the refined rough k-means algorithm. We use a synthetic data set and a real-world data set to verify that the new technique for local outliers detection is not only accurate but also efficient.

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2011 3rd International Workshop on Intelligent Systems and Applications

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