Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6939027 | Pattern Recognition | 2018 | 32 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
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Computer Vision and Pattern Recognition
Authors
Brijnesh J. Jain,