等聚类:局部数据聚类的一种通用框架

2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) Pub Date : 2019-12-01 DOI:10.1109/ICMLA.2019.00058

David Haley, Ehsan Kamalinejad, Jiaofei Zhong

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引用次数: 0

摘要

本文提出了一种基于等周不等式的广义局部聚类框架。我们还证明，当代方法包括在其范围内，它可以容纳不同类型的数据集，包括那些重叠的社区。然后，我们使用新框架提出了一个高效的贪婪算法，并将新算法的输出与现有方法进行了比较。

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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IsoClustering: A Generalized Framework for Local Data Clustering

In this paper, we propose a generalized framework for local clustering based on isoperimetric inequalities. We also demonstrate that contemporary approaches are included in its scope and that it can accommodate data sets of different types, including those with overlapping communities. We then present an efficient, greedy algorithm using the new framework and compare the output of the new algorithm with existing methods.

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2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)

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