Unifying Density-Based Clustering and Outlier Detection

Yunxin Tao, D. Pi
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引用次数: 19

Abstract

Density-based clustering and density-based outlier detection have been extensively studied in the data mining. However, Existing works address density-based clustering or density-based outlier detection solely. But for many scenarios, it is more meaningful to unify density-based clustering and outlier detection when both the clustering and outlier detection results are needed simultaneously. In this paper, a novel algorithm named DBCOD that unifies density-based clustering and outlier detection is proposed. In order to discover density-based clusters and assign to each outlier a degree of being an outlier, a novel concept called neighborhood-based local density factor (NLDF) is employed. The experimental results on different shape, large-scale, and high-dimensional databases demonstrate the effectiveness and efficiency of our method.
统一基于密度的聚类和离群点检测
基于密度的聚类和基于密度的离群点检测在数据挖掘中得到了广泛的研究。然而,现有的工作只涉及基于密度的聚类或基于密度的离群点检测。但在很多场景下,当同时需要聚类和离群点检测结果时,统一基于密度的聚类和离群点检测更有意义。本文提出了一种将基于密度的聚类和离群点检测相结合的DBCOD算法。为了发现基于密度的聚类,并为每个离群值分配离群值的程度,采用了基于邻域的局部密度因子(NLDF)的新概念。在不同形状、大规模和高维数据库上的实验结果证明了该方法的有效性和高效性。
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