Self-Adjusted Consensus Clustering with Agglomerate Algorithms

IF 0.6 4区计算机科学 Q4 AUTOMATION & CONTROL SYSTEMS

Automation and Remote Control Pub Date : 2024-08-06 DOI:10.1134/s0005117924030044

B. G. Mirkin, A. A. Parinov

引用次数: 0

Abstract

This paper reports of theoretical and computational results related to an original concept of consensus clustering involving what we call the projective distance between partitions. This distance is defined as the squared difference between a partition incidence matrix and its image over the orthogonal projection in the linear space spanning the other partition incidence matrix. It appears, provided that the ensemble clustering is of a sufficient size, agglomerate clustering with the semi-average within-cluster similarity criterion effectively solves the problem of consensus partition and, moreover, of the number of clusters in it.

查看原文本刊更多论文

利用聚类算法进行自调整共识聚类

摘要本文报告了与共识聚类的原创概念有关的理论和计算结果，涉及我们所说的分区之间的投影距离。这个距离被定义为一个分区入射矩阵与其在跨另一个分区入射矩阵的线性空间中的正交投影上的图像之间的平方差。由此看来，只要集合聚类的规模足够大，采用半平均簇内相似性准则的集合聚类就能有效解决共识分区的问题，而且还能解决其中的簇数问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Automation and Remote Control 工程技术-仪器仪表

CiteScore

1.70

自引率

28.60%

发文量

审稿时长

3-8 weeks

期刊介绍： Automation and Remote Control is one of the first journals on control theory. The scope of the journal is control theory problems and applications. The journal publishes reviews, original articles, and short communications (deterministic, stochastic, adaptive, and robust formulations) and its applications (computer control, components and instruments, process control, social and economy control, etc.).

文献相关原料

公司名称	产品信息	采购帮参考价格