基于三角形周长的聚类结构点排序新算法:OPTICS(BOPT)

H. K. Kalita, D. Bhattacharyya, A. Kar
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引用次数: 12

摘要

将一组物理或抽象对象分组(或划分)为相似对象的类的过程称为聚类。通过应用聚类方法可以识别密集和稀疏区域,发现数据集中数据之间的总体分布模式和有趣的相关性。本文提出了一种新的基于三角形周长的半自动聚类算法——点排序算法:OPTICS(BOPT)。我们的方法既可以用于数字数据,也可以用于分类数据,并且可以逐部分地显示聚类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A New Algorithm for Ordering of Points to Identify Clustering Structure Based on Perimeter of Triangle: OPTICS(BOPT)
The process of grouping (or partitioning) a set of physical or abstract objects into classes of similar objects is known as clustering. By applying clustering method it is possible to identify dense & sparse regions, discover overall distribution pattern and interesting correlations amongst data in a data set. In this paper we propose a new, semiautomatic clustering algorithm called-ordering of points to identify clustering structure based on perimeter of triangle : OPTICS(BOPT). Our method can be used in both numeric as well as categorical data and shows clusters part-by-part.
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