Incremental Clustering for Hierarchical Clustering

K. Narita, T. Hochin, Hiroki Nomiya
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引用次数: 3

Abstract

This paper proposes a clustering algorithm for updating clusters without reclustering when a point is inserted. We define the center and the radius of the cluster, and update clustering results of points using them. We introduce the concept of outliers and also consider the change in the number of clusters caused by data insertion. From comparative experiments with reclustering by the conventional method, it is shown that the proposed method can cluster points with short calculation time.
分层聚类的增量聚类
本文提出了一种新的聚类算法,用于在插入点时更新聚类而不重新聚类。我们定义了聚类的中心和半径,并利用它们更新点的聚类结果。我们引入了异常值的概念,并考虑了数据插入引起的聚类数量的变化。通过与传统聚类方法的对比实验表明,该方法可以在较短的计算时间内聚类。
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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