聚类分析
计算机科学
分拆(数论)
等级制度
层次聚类
度量(数据仓库)
星团(航天器)
数据挖掘
理论(学习稳定性)
单连锁聚类
树(集合论)
网络的层次聚类
完整的链接聚类
相关聚类
算法
CURE数据聚类算法
人工智能
数学
机器学习
市场经济
组合数学
数学分析
经济
程序设计语言
作者
Ricardo J. G. B. Campello,Davoud Moulavi,Jöerg Sander
标识
DOI:10.1007/978-3-642-37456-2_14
摘要
We propose a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed. For obtaining a “flat” partition consisting of only the most significant clusters (possibly corresponding to different density thresholds), we propose a novel cluster stability measure, formalize the problem of maximizing the overall stability of selected clusters, and formulate an algorithm that computes an optimal solution to this problem. We demonstrate that our approach outperforms the current, state-of-the-art, density-based clustering methods on a wide variety of real world data.
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