arXiv research

Paper introduces a method for supervised hierarchical clustering with Exponential Linkage.

Many modern clustering methods scale well to a large number of data items, N, but not to a large number of clusters, K. This paper introduces PERCH, a new non-greedy algorithm for online hierarchical clustering that scales to both massive N and K--a problem setting we term extreme clustering. Our algorithm efficiently …

Improves hierarchical clustering in Euclidean space using autoencoders.

Dendrograms used in data analysis are ultrametric spaces, hence objects of nonarchimedean geometry. It is known that there exist $p$-adic representation of dendrograms. Completed by a point at infinity, they can be viewed as subtrees of the Bruhat-Tits tree associated to the $p$-adic projective line. The implications a…

New method uses dendrograms for better mixture model selection and clustering.

A conceptual framework for cluster analysis from the viewpoint of p-adic geometry is introduced by describing the space of all dendrograms for n datapoints and relating it to the moduli space of p-adic Riemannian spheres with punctures using a method recently applied by Murtagh (2004b). This method embeds a dendrogram …

Hierarchical graph clustering is a common technique to reveal the multi-scale structure of complex networks. We propose a novel metric for assessing the quality of a hierarchical clustering. This metric reflects the ability to reconstruct the graph from the dendrogram, which encodes the hierarchy. The optimal represent…

We derive a statistical model for estimation of a dendrogram from single linkage hierarchical clustering (SLHC) that takes account of uncertainty through noise or corruption in the measurements of separation of data. Our focus is on just the estimation of the hierarchy of partitions afforded by the dendrogram, rather t…

Hierarchical clustering uses OWA operators to generalize linkage methods and avoid dendrogram inversions.

We propose unsupervised representation learning and feature extraction from dendrograms. The commonly used Minimax distance measures correspond to building a dendrogram with single linkage criterion, with defining specific forms of a level function and a distance function over that. Therefore, we extend this method to …

This work introduces novel methods to identify and compare cycles across topological objects.

An efficient method to compute a single linkage dendrogram.

All the connections, pure toward the nilpotent structure, are found. Examples of manifolds, for which the curvature tensor is pure or hybrid, are given. For a manifold of B-type a necessary and sufficient condition for purity of the curvature tensor is proved. It is verified that the conformal change of the metric of a…

Previously, we proposed a physically-inspired method to construct data points into an effective in-tree (IT) structure, in which the underlying cluster structure in the dataset is well revealed. Although there are some edges in the IT structure requiring to be removed, such undesired edges are generally distinguishable…

This paper examines how regional trade agreements affect global trade relationships.

Method preserves order in hierarchical clustering of ordered data.

Point source detection at low signal-to-noise is challenging for astronomical surveys, particularly in radio interferometry images where the noise is correlated. Machine learning is a promising solution, allowing the development of algorithms tailored to specific telescope arrays and science cases. We present DeepSourc…

Unified framework for SGMoE resolves estimation and selection issues.

Proposes a revenue function to evaluate dendrograms from comparisons.

This work improved clustering methods by analyzing various datasets and dendrograms.

QNA uses quantum-inspired density operators to diagnose market dependence and structural risk.

We present a large catalog of optically selected galaxy clusters from the application of a new Gaussian Mixture Brightest Cluster Galaxy (GMBCG) algorithm to SDSS Data Release 7 data. The algorithm detects clusters by identifying the red sequence plus Brightest Cluster Galaxy (BCG) feature, which is unique for galaxy c…

funLOCI identifies clusters in functional data.

Bagging and boosting are proved to be the best methods of building multiple classifiers in classification combination problems. In the area of "flat clustering" problems, it is also recognized that multi-clustering methods based on boosting provide clusterings of an improved quality. In this paper, we introduce a novel…

This paper characterizes hierarchical clustering methods that abide by two previously introduced axioms -- thus, denominated admissible methods -- and proposes tractable algorithms for their implementation. We leverage the fact that, for asymmetric networks, every admissible method must be contained between reciprocal …

Paper proposes a novel method to accurately determine the number of experts in Gaussian-gated Gaussian MoE models.

Geometric observables detect financial regime shifts with high accuracy.

The study classifies normal subgroups of mapping class groups of surfaces with Cantor subsets.

New theorem for deep neural networks improves classification margins.

In the age of globalization, it is natural that the stock market of each country is not independent form the other markets. In this case, collective behavior could be emerged form their dependency together. This article studies the collective behavior of a set of forty influential markets in the world economy with the …

RegMixMatch optimizes Mixup for semi-supervised learning by integrating high- and low-confidence samples.

Here, we propose a clustering technique for general clustering problems including those that have non-convex clusters. For a given desired number of clusters $K$, we use three stages to find a clustering. The first stage uses a hybrid clustering technique to produce a series of clusterings of various sizes (randomly se…

A clustering algorithm based on the Hausdorff distance is introduced and compared to the single and complete linkage. The three clustering procedures are applied to a toy example and to the time series of financial data. The dendrograms are scrutinized and their features confronted. The Hausdorff linkage relies of firm…

cuSLINK clusters data faster on GPUs, saving space and time.

WS-II algorithm segments trajectories with high accuracy.

Graph learning categorizes DeFi services into similar functionalities.

Tangles improve clustering in various datasets.

Decision trees can be biased towards minority class, contrary to belief.

This paper proposes a new dimensionality reduction algorithm named branching embedding (BE). It converts a dendrogram to a two-dimensional scatter plot, and visualizes the inherent structures of the original high-dimensional data. Since the conversion part is not computationally demanding, the BE algorithm would be ben…

Proposes HypCSE for enhanced hierarchical clustering.

Odd-dimensional Riemannian manifolds admit pure spin-c Killing spinors if and only if they are α-Sasakian.

Hierarchical clustering is a class of algorithms that seeks to build a hierarchy of clusters. It has been the dominant approach to constructing embedded classification schemes since it outputs dendrograms, which capture the hierarchical relationship among members at all levels of granularity, simultaneously. Being gree…

Framework uses machine learning to distinguish major COVID-19 variants.

This paper shows hypercommutative algebras on Calabi-Yau manifolds are formal.

New subspace prototype flag median improves clustering on noisy data.

Graph auto-encoders improve financial clustering using news and stock data.

We fix integers $k> 0$ and $n>0$. For a $k$-punctured Riemann surface $Σ\setminus \{ p_1,\ldots,p_k \}$ and a $k$-tuple $\boldsymbolμ=(μ^1,\ldots,μ^k)$ of partitions of $n$, we can define the character variety of type $\boldsymbolμ$. In this paper, we consider the case where $Σ=\mathbb{P}^1$ and $\boldsymbolμ$ is indiv…

In this work we propose a simple and easily parallelizable algorithm for multiway graph partitioning. The algorithm alternates between three basic components: diffusing seed vertices over the graph, thresholding the diffused seeds, and then randomly reseeding the thresholded clusters. We demonstrate experimentally that…