Persistent Clustering and a Theorem of J. Kleinberg
| dc.creator | Carlsson, Gunnar | |
| dc.creator | Memoli, Facundo | |
| dc.date | 2008-08-16 | |
| dc.date.accessioned | 2026-07-07T09:57:02Z | |
| dc.date.available | 2026-07-07T09:57:02Z | |
| dc.description | We construct a framework for studying clustering algorithms, which includes two key ideas: persistence and functoriality. The first encodes the idea that the output of a clustering scheme should carry a multiresolution structure, the second the idea that one should be able to compare the results of clustering algorithms as one varies the data set, for example by adding points or by applying functions to it. We show that within this framework, one can prove a theorem analogous to one of J. Kleinberg, in which one obtains an existence and uniqueness theorem instead of a non-existence result. We explore further properties of this unique scheme, stability and convergence are established. | |
| dc.identifier | https://arxiv.org/abs/0808.2241 | |
| dc.identifier | http://arxiv.org/abs/0808.2241 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/167197 | |
| dc.subject | Machine Learning | |
| dc.title | Persistent Clustering and a Theorem of J. Kleinberg | |
| dc.type | text |