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Functorial hierarchical clustering with overlaps
- Source :
- Discrete Applied Mathematics. 236:108-123
- Publication Year :
- 2018
- Publisher :
- Elsevier BV, 2018.
-
Abstract
- This work draws inspiration from three important sources of research on dissimilarity-based clustering and intertwines those three threads into a consistent principled functorial theory of clustering. Those three are the overlapping clustering of Jardine and Sibson, the functorial approach of Carlsson and M\'{e}moli to partition-based clustering, and the Isbell/Dress school's study of injective envelopes. Carlsson and M\'{e}moli introduce the idea of viewing clustering methods as functors from a category of metric spaces to a category of clusters, with functoriality subsuming many desirable properties. Our first series of results extends their theory of functorial clustering schemes to methods that allow overlapping clusters in the spirit of Jardine and Sibson. This obviates some of the unpleasant effects of chaining that occur, for example with single-linkage clustering. We prove an equivalence between these general overlapping clustering functors and projections of weight spaces to what we term clustering domains, by focusing on the order structure determined by the morphisms. As a specific application of this machinery, we are able to prove that there are no functorial projections to cut metrics, or even to tree metrics. Finally, although we focus less on the construction of clustering methods (clustering domains) derived from injective envelopes, we lay out some preliminary results, that hopefully will give a feel for how the third leg of the stool comes into play.<br />Comment: Minor revisions. 24 pages, 1 figure
- Subjects :
- FOS: Computer and information sciences
Clustering high-dimensional data
Computer Science - Machine Learning
Fuzzy clustering
Correlation clustering
Conceptual clustering
Machine Learning (stat.ML)
0102 computer and information sciences
Mathematics::Algebraic Topology
01 natural sciences
Machine Learning (cs.LG)
Combinatorics
H.3.3
Statistics - Machine Learning
Mathematics::Category Theory
Discrete Mathematics and Combinatorics
0101 mathematics
Cluster analysis
Mathematics
Brown clustering
Applied Mathematics
010102 general mathematics
51K05, 68P01
Hierarchical clustering
010201 computation theory & mathematics
FLAME clustering
Subjects
Details
- ISSN :
- 0166218X
- Volume :
- 236
- Database :
- OpenAIRE
- Journal :
- Discrete Applied Mathematics
- Accession number :
- edsair.doi.dedup.....bea4f9d0e39f85920538099d6f0a9152
- Full Text :
- https://doi.org/10.1016/j.dam.2017.10.015