The Mean Partition Theorem in consensus clustering

PATTERN RECOGNITION

卷:79

Article

Jain, Brijnesh J.¹ 

[1] TU Berlin, Ernst Reuter Pl 7, D-10587 Berlin, Germany

关键词: Cluster ensembles;    Consensus clustering;    Mean partition;    Optimal multiple alignment;    Profiles;    Motifs;    Stability;    Diversity;   

DOI  :  10.1016/j.patcog.2018.01.030

来源: Elsevier

【 摘 要 】

This article presents the Mean Partition Theorem of consensus clustering. We show that the Mean Partition Theorem is a fundamental result that connects to different, but not obviously related branches such as: (i) optimization, (ii) statistical consistency, (iii) optimal multiple alignment, (iv) profiles and motifs, (v) cluster stability, (vi) diversity, and (vii) Condorcet's Jury Theorem. All proofs rest on the orbit space framework. The implications are twofold: First, the Mean Partition Theorem plays a far-reaching and central role in consensus clustering. Second, orbit spaces constitute a convenient representation for gaining insight into partition spaces. (C) 2018 Elsevier Ltd. All rights reserved.

【 授权许可】

Free