Results 11 to 20 of about 1,195,838 (307)

Information Theoretic Hierarchical Clustering [PDF]

Entropy, 2011
Hierarchical clustering has been extensively used in practice, where clusters can be assigned and analyzed simultaneously, especially when estimating the number of clusters is challenging.
Babak Nadjar Araabi, Mehdi Aghagolzadeh, Hamid Soltanian-Zadeh   +2 more
doaj   +3 more sources

Hierarchical clustering in astronomy

Astronomy and Computing, 2022
12 pages, 8 figures, accepted by Astronomy and ...
Heng Yu, Xiaolan Hou
openaire   +3 more sources

Hierarchical Means Clustering

Journal of Classification, 2022
AbstractIn the cluster analysis literature, there are several partitioning (non-hierarchical) methods for clustering multivariate objects based on model estimation. Distinct to these methods is the use of a system of n nested statistical models and the optimization of a loss function to best-fit a clustering model to observed data.
Maurizio Vichi, Carlo Cavicchia, Patrick J. F. Groenen   +2 more
openaire   +2 more sources

Hierarchical Clustering via Sketches and Hierarchical Correlation Clustering

CoRR, 2021
Recently, Hierarchical Clustering (HC) has been considered through the lens of optimization. In particular, two maximization objectives have been defined. Moseley and Wang defined the \emph{Revenue} objective to handle similarity information given by a weighted graph on the data points (w.l.o.g., $[0,1]$ weights), while Cohen-Addad et al.
Danny Vainstein, Vaggos Chatziafratis, Gui Citovsky, Anand Rajagopalan, Mohammad Mahdian, Yossi Azar   +5 more
openaire   +3 more sources

Sequential Hierarchical Pattern Clustering [PDF]

, 2009
Clustering is a widely used unsupervised data analysis technique in machine learning. However, a common requirement amongst many existing clustering methods is that all pairwise distances between patterns must be computed in advance.
Niranjan, Mahesan, Farran, Bassam, Ramanan, Amirthalingam   +2 more
core   +2 more sources

Contrastive Hierarchical Clustering

, 2023
Deep clustering has been dominated by flat models, which split a dataset into a predefined number of groups. Although recent methods achieve an extremely high similarity with the ground truth on popular benchmarks, the information contained in the flat partition is limited.
Znalezniak, Michał, Rola, Przemysław, Kaszuba, Patryk, Tabor, Jacek, Śmieja, Marek   +4 more
openaire   +3 more sources

jHc: A Java desktop application for agglomerative hierarchical clustering [PDF]

, 2023
jHc is a standalone Java desktop application that allows the user to subject data to different approaches of agglomerative hierarchical ...
Hofmann, Andreas
core   +1 more source

Fair Hierarchical Clustering

CoRR, 2020
As machine learning has become more prevalent, researchers have begun to recognize the necessity of ensuring machine learning systems are fair. Recently, there has been an interest in defining a notion of fairness that mitigates over-representation in traditional clustering. In this paper we extend this notion to hierarchical clustering, where the goal
Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar 0001, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang   +8 more
openaire   +3 more sources

R/BHC : fast Bayesian hierarchical clustering for microarray data [PDF]

, 2009
Background: Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data analysis, little attention has been paid to uncertainty in the results obtained ...
Grant, Murray, Katherine J Denby, Savage, R, Savage, Richard S., Xu, Yang, Denby Katherine J, Denby, Katherine J., David L Wild, Ghahramani, Z, Grant Murray, Richard S Savage, Truman, W, Truman, William M., Grant, M, Wild, David L., Murray Grant, Denby, K, Savage Richard S, William M Truman, Xu, Y, Ghahramani Zoubin, Truman William M, Heller Katherine, Katherine Heller, ,, Wild David L, Heller, KA, Heller, K. (Katherine), Yang Xu, Wild, DL, Ghahramani, Zoubin, Zoubin Ghahramani, Xu Yang   +32 more
core   +13 more sources

Robust Hierarchical Clustering

CoRR, 2013
One of the most widely used techniques for data clustering is agglomerative clustering. Such algorithms have been long used across many different fields ranging from computational biology to social sciences to computer vision in part because their output is easy to interpret.
Maria-Florina Balcan, Yingyu Liang, Pramod Gupta   +2 more
openaire   +4 more sources