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首页> 外文期刊>Applied Soft Computing >DBCAMM: A novel density based clustering algorithm via using the Mahalanobis metric
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DBCAMM: A novel density based clustering algorithm via using the Mahalanobis metric

机译：DBCAMM：一种通过使用Mahalanobis度量的基于密度的新颖聚类算法

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摘要

In this paper we propose a new density based clustering algorithm via using the Mahalanobis metric. This is motivated by the current state-of-the-art density clustering algorithm DBSCAN and some fuzzy clustering algorithms. There are two novelties for the proposed algorithm: One is to adopt the Mahalanobis metric as distance measurement instead of the Euclidean distance in DBSCAN and the other is its effective merging approach for leaders and followers defined in this paper. This Mahalanobis metric is closely associated with dataset distribution. In order to overcome the unique density issue in DBSCAN, we propose an approach to merge the sub-clusters by using the local sub-cluster density information. Eventually we show how to automatically and efficiently extract not only 'traditional' clustering information, such as representative points, but also the intrinsic clustering structure. Extensive experiments on some synthetic datasets show the validity of the proposed algorithm. Further the segmentation results on some typical images by using the proposed algorithm and DBSCAN are presented in this paper and they are shown that the proposed algorithm can produce much better visual results in image segmentation.
机译：在本文中，我们通过使用Mahalanobis度量提出了一种新的基于密度的聚类算法。这是由当前最新的密度聚类算法DBSCAN和一些模糊聚类算法引起的。该算法有两个新颖之处：一是采用马氏距离度量代替DBSCAN中的欧几里得距离，其二是本文定义的领导者和追随者的有效合并方法。该Mahalanobis指标与数据集分布紧密相关。为了克服DBSCAN中唯一的密度问题，我们提出了一种使用局部子集群密度信息合并子集群的方法。最终，我们展示了如何不仅自动且有效地提取“传统”聚类信息（例如代表点），还包括内在聚类结构。在一些综合数据集上的大量实验证明了该算法的有效性。进一步提出了使用该算法和DBSCAN对某些典型图像进行分割的结果，结果表明该算法在图像分割中可以产生更好的视觉效果。

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