Anna Szymkowiak Have, Mark A. Girolami, Jan Larsen
Abstract: Methods for spectral clustering have been proposed
recently which rely on the eigenvalue decomposition of an affinity
matrix. In this work it is proposed that the affinity matrix
is created based on the elements of a non-parametric density
estimator. This matrix is then decomposed to obtain posterior
probabilities of class membership using an appropriate form of
nonnegative matrix factorization. The troublesome selection of
hyperparameters such as kernel width and number of clusters
can be obtained using standard cross-validation methods as is
demonstrated on a number of diverse data sets.
Agglomerative Hierarchical Clustering with Constraints: Theoretical and Empirical Results
Ian Davidson1 and S.S. Ravi1
(1) Department of Computer Science, University at Albany - State University of New York, Albany, NY 12222,
M. Beck, J. Spoerhase, und S. Storandt. Proc. 9th International Conference on Algorithms and Discrete Applied Mathematics (CALDAM'23), 13947, Seite 321--334. (2023)
P. Sanchez, und L. Dietz. Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, Seite 132-142. ACM, (Juli 2022)Assessing the value of RecSys you need to distinguish user types - and it can be done by clustering.
M. KARPEN, D. TOBIAS, und C. BROOKS. BIOCHEMISTRY, 32 (2):
412-420(Januar 1993)Statistical clustering techniques for the analysis of long molecular dynamics trajectories: analysis of 2.2-ns trajectories of YPGDV
Mary E. Karpen, Douglas J. Tobias, Charles L. Brooks
pp 412–420
Publication Date: January 1993 ()
DOI: 10.1021/bi00053a005.