基于物理定律的密度聚类与层次聚类的调和

Nader Bazyari, H. Sajedi
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引用次数: 0

摘要

本文在学者们提出的所有优秀的数据聚类算法的启发下,提出了一种新的数据处理方法。驱动这种方法的主要主题是混合层次聚类方法和高斯估计方法,以发现使用传统带宽估计器无法到达的数据中的隐藏结构。相反,评估数据相似性的标准是牛顿物理学原理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Reconcile of Density Based and Hierarchical Clustering Based on the Laws of Physics
In this paper a new approach toward data processing is proposed that is inspired by all the prominent data clustering algorithms proposed by scholars. The main motif that drove this approach was to mix hierarchical clustering methods with Gaussian Estimators as to find a hidden structure in data that was not reachable using traditional bandwidth estimators. Instead the criteria for assessing similarity among data was the principles for Newtonian Physics.
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