检索结果-内蒙古大学图书馆

Autonomous clustering by fast find of mass and distance peaks

IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第07期47卷 5336-5349页

作者： Yang, Jie Lin, Chin-Teng Australian AI Institute FEIT University of Technology Sydney Computational Intelligence and Brain Computer Interface Lab Australia

Clustering is an essential analytical tool across a wide range of scientific fields, including biology, chemistry, astronomy, and pattern recognition. This paper introduces a novel clustering algorithm as a competitive alternative to existing methods, based on the intuitive principle that a cluster should merge with its nearest neighbor with a higher mass, unless both clusters have relatively large masses and the distance between them is also substantial. By identifying peaks in mass and distance, the algorithm effectively detects and removes incorrect mergers. The proposed method is entirely parameter-free, enabling it to autonomously recognize various cluster types, determine the optimal number of clusters, and identify noise. Extensive experiments on synthetic and real-world data sets demonstrate the algorithm's versatility and consistently strong performance compared to other state-of-the-art methods. © 1979-2012 IEEE.

关键词： Clustering algorithms Partitioning algorithms Noise Nearest Neighbor Methods Merging Torque Biology Approximation algorithms Accuracy High Dimensional Data Clustering Parameter Free Data Analysis Unsupervised Learning Clustering Algorithm Real World Data Large Mass Real World Datasets Hierarchical Clustering Distance Matrix Image Dataset Density Estimation Cutoff Distance Agglomerative Clustering Challenging Dataset Average Rank Hierarchical Algorithm High Dimensional Datasets Abnormal Connectivity Hierarchical Tree Hierarchical Clustering Algorithm recent algorithms Square Of The Distance Agglomerative Method Automatic Determination Cluster Mass Automatic Clustering Global Mean Value Optical Character Recognition Hierarchical Clustering Method Cell Tracking Partitional Clustering Comprehensive Evaluation Image Recognition

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：