Tootfinder

This https://arxiv.org/abs/2403.01684 has been replaced.
link: https://scholar.google.com/scholar?q=a

Dendrogram of mixing measures: Hierarchical clustering and model selection for finite mixture models
We present a new way to summarize and select mixture models via the hierarchical clustering tree (dendrogram) constructed from an overfitted latent mixing measure. Our proposed method bridges agglomerative hierarchical clustering and mixture modeling. The dendrogram's construction is derived from the theory of convergence of the mixing measures, and as a result, we can both consistently select the true number of mixing components and obtain the pointwise optimal convergence rate for parameter e…

This https://arxiv.org/abs/2303.10117 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Estimation of Grouped Time-Varying Network Vector Autoregression Models
This paper introduces a flexible time-varying network vector autoregressive model framework for large-scale time series. A latent group structure is imposed on the heterogeneous and node-specific time-varying momentum and network spillover effects so that the number of unknown time-varying coefficients to be estimated can be reduced considerably. A classic agglomerative clustering algorithm with nonparametrically estimated distance matrix is combined with a ratio criterion to consistently estim…

This https://arxiv.org/abs/2305.15022 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Hierarchical clustering with dot products recovers hidden tree structure
In this paper we offer a new perspective on the well established agglomerative clustering algorithm, focusing on recovery of hierarchical structure. We recommend a simple variant of the standard algorithm, in which clusters are merged by maximum average dot product and not, for example, by minimum distance or within-cluster variance. We demonstrate that the tree output by this algorithm provides a bona fide estimate of generative hierarchical structure in data, under a generic probabilistic gra…

It's Hard to HAC with Average Linkage!
MohammadHossein Bateni, Laxman Dhulipala, Kishen N Gowda, D Ellis Hershkowitz, Rajesh Jayaram, Jakub {\L}\k{a}cki
https://arxiv.org/abs/2404.14730

It's Hard to HAC with Average Linkage!
Average linkage Hierarchical Agglomerative Clustering (HAC) is an extensively studied and applied method for hierarchical clustering. Recent applications to massive datasets have driven significant interest in near-linear-time and efficient parallel algorithms for average linkage HAC. We provide hardness results that rule out such algorithms. On the sequential side, we establish a runtime lower bound of $n^{3/2-ε}$ on $n$ node graphs for sequential combinatorial algorithms under standard fin…

Motion Prediction of Multi-agent systems with Multi-view clustering
Anegi James, Efstathios Bakolas
https://arxiv.org/abs/2403.13905 https://

Motion Prediction of Multi-agent systems with Multi-view clustering
This paper presents a method for future motion prediction of multi-agent systems by including group formation information and future intent. Formation of groups depends on a physics-based clustering method that follows the agglomerative hierarchical clustering algorithm. We identify clusters that incorporate the minimum cost-to-go function of a relevant optimal control problem as a metric for clustering between the groups among agents, where groups with similar associated costs are assumed to b…

Happy and Immersive Clustering Segmentations of Biological Co-Expression Patterns
Richard Tj\"ornhammar
https://arxiv.org/abs/2402.06928 https://

Happy and Immersive Clustering Segmentations of Biological Co-Expression Patterns
In this work, we present an approach for evaluating segmentation strategies and solving the biological problem of creating robust interpretable maps of biological data by employing wards agglomerative hierarchical clustering applied to coexpression coordinates to deduce a faithful representation of the input. We adopt and quantify two analyte-centric metrics named happiness and immersiveness, one for describing the suitability of a single analyte concerning the segmentation as well as a secon…