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Hierarchical clustering in astronomy
Astronomy and Computing, 2022 12 pages, 8 figures, accepted by Astronomy and ...
Heng Yu, Xiaolan Hou
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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 +2 more
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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 +5 more
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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 +4 more
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HIERARCHICAL SPHERICAL CLUSTERING
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2002 This work introduces an alternative representation for large dimensional data sets. Instead of using 2D or 3D representations, data is located on the surface of a sphere. Together with this representation, a hierarchical clustering algorithm is defined to analyse and extract the structure of the data.
Vicenç Torra, Sadaaki Miyamoto
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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 +8 more
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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 +2 more
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Hierarchical Adaptive Clustering [PDF]
Informatica, 2008 This paper studies an adaptive clustering problem. We focus on re-clustering an object set, previously clustered, when the feature set characterizing the objects increases. We propose an adaptive clustering method based on a hierarchical agglomerative approach, Hierarchical Adaptive Clustering (HAC), that adjusts the partitioning into clusters that was
Gabriela Serban, Alina Campan
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Complementary hierarchical clustering [PDF]
Biostatistics, 2007 When applying hierarchical clustering algorithms to cluster patient samples from microarray data, the clustering patterns generated by most algorithms tend to be dominated by groups of highly differentially expressed genes that have closely related expression patterns.
Gen, Nowak, Robert, Tibshirani
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Belief Hierarchical Clustering [PDF]
, 2014 In the data mining field many clustering methods have been proposed, yet standard versions do not take into account uncertain databases. This paper deals with a new approach to cluster uncertain data by using a hierarchical clustering defined within the belief function framework.
Maalel, Wiem +3 more
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