5 ms·
I would say there are two aspects of clustering that are important: accuracy of the model, and accuracy of the model fitting process. A model is a guess about
by Xcelerate 11y ago
I would say there are two aspects of clustering that are important: accuracy of the model, and accuracy of the model fitting process.
A model is a guess about the underlying process that "generates" the data. If you're trying to use hyperplanes to divide data that lies on a manifold, then you are going to have poor results no matter how good your fitting algorithm is.
On the other hand, even if you know the true model, high levels of noise can prevent you from recovering the correct parameters. For instance, Max-Cut is NP-hard, and the best we can do is a semidefinite programming approximation. Beyond a certain noise threshold, the gap between the SDP solution and the true solution becomes very large very quickly.
- leecarraher 11y agosoft margin SVM would be useful here. And there are manifold aware topographical clustering algorithms, like Persistent Homology techniques, Gunnar Carlson is the man on this new clustering offshoot.
- lmcinnes 11y agoOddly enough HDBSCAN can be recast as a persistent homology computation; the trick is in simplicial complex construction; you need something slightly more density aware than traditional Rips complexes. I am currently working on designing a version of Carlsson and Memoli's Multiparameter Hierarchical Clustering based on the persistent homology interpretation of HDBSCAN.