A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
SESSC clusters fuzzy rules for TSK classifiers, improving performance with label info.
Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm which has the high-performance rate for dataset where clusters have the constant density of data points. One of the significant attributes of this algorithm is noise cancellation. However, DBSCAN demonstrates reduced per…
Recently, deep clustering, which is able to perform feature learning that favors clustering tasks via deep neural networks, has achieved remarkable performance in image clustering applications. However, the existing deep clustering algorithms generally need the number of clusters in advance, which is usually unknown in…
Cluster-wise linear regression (CLR), a clustering problem intertwined with regression, is to find clusters of entities such that the overall sum of squared errors from regressions performed over these clusters is minimized, where each cluster may have different variances. We generalize the CLR problem by allowing each…
In this work we present a clustering technique called \textit{multi-level conformal clustering (MLCC)}. The technique is hierarchical in nature because it can be performed at multiple significance levels which yields greater insight into the data than performing it at just one level. We describe the theoretical underpi…
Benchmarked over 70 graph clustering algorithms.
Deep clustering has increasingly been demonstrating superiority over conventional shallow clustering algorithms. Deep clustering algorithms usually combine representation learning with deep neural networks to achieve this performance, typically optimizing a clustering and non-clustering loss. In such cases, an autoenco…
Proposes CI-GMVC to improve graph-based multi-view clustering performance.
Meta-learning neural networks for better clustering representations.
Study clusters Kenyan medical insurance companies based on financial performance and reporting consistency.
Subspace clustering refers to the problem of clustering high-dimensional data points into a union of low-dimensional linear subspaces, where the number of subspaces, their dimensions and orientations are all unknown. In this paper, we propose a variation of the recently introduced thresholding-based subspace clustering…
Integrates VAEs into EM for deep clustering and generation.
Proposes Contrastive Clustering for improved clustering performance.
Elastic co-clustering improves clustering of single-cell genomic data.
New algorithm IAC recovers hidden communities in labeled SBM with optimal performance.
Paper improves MFC algorithm for clustering linear subspaces.
VC-PCR improves prediction by clustering correlated variables.
Unified Bayesian framework for clustered federated learning improves model performance.
We consider the task of estimating a Gaussian graphical model in the high-dimensional setting. The graphical lasso, which involves maximizing the Gaussian log likelihood subject to an l1 penalty, is a well-studied approach for this task. We begin by introducing a surprising connection between the graphical lasso and hi…
New distributed clustering algorithms show resilience to initialization issues.
Clustering explores meaningful patterns in the non-labeled data sets. Cluster Ensemble Selection (CES) is a new approach, which can combine individual clustering results for increasing the performance of the final results. Although CES can achieve better final results in comparison with individual clustering algorithms…
Convex clustering, a convex relaxation of k-means clustering and hierarchical clustering, has drawn recent attentions since it nicely addresses the instability issue of traditional nonconvex clustering methods. Although its computational and statistical properties have been recently studied, the performance of convex c…
Paper proposes a new method to improve clustering ensemble performance.
Generating interpretable visualizations from complex data is a common problem in many applications. Two key ingredients for tackling this issue are clustering and representation learning. However, current methods do not yet successfully combine the strengths of these two approaches. Existing representation learning mod…
DPMM-CFL clusters clients for federated learning without fixed K, improving performance.
We study in this paper the problem of jointly clustering and learning representations. As several previous studies have shown, learning representations that are both faithful to the data to be clustered and adapted to the clustering algorithm can lead to better clustering performance, all the more so that the two tasks…
Simplified image clustering achieves competitive results without text-based embeddings.
New computational lower bounds for clustering and related problems.
Meta clustering categorizes learners for collaborative learning.
This study evaluates clustering algorithms on high-dimensional data.
iCVI-ARTMAP accelerates clustering with adaptive resonance theory and validity indices.
Clustering is one of the most fundamental and wide-spread techniques in exploratory data analysis. Yet, the basic approach to clustering has not really changed: a practitioner hand-picks a task-specific clustering loss to optimize and fit the given data to reveal the underlying cluster structure. Some types of losses--…
Proposes a constraint for deep clustering to handle both simple and complex topologies.
Improved spectral clustering with fewer eigenvectors performs better.
A new clustering algorithm fuses heat diffusion and turning angle for robustness.
Paper provides a performance guarantee for spectral clustering.
t-NEB clusters high-dimensional data hierarchically with density paths.
As the size of datasets become massive, many commonly-used clustering algorithms (for example, -means or hierarchical agglomerative clustering (HAC) require prohibitive computational cost and memory. In this paper, we propose a solution to these clustering problems by extending threshold clustering (TC) to probl…
Document clustering and topic modeling are two closely related tasks which can mutually benefit each other. Topic modeling can project documents into a topic space which facilitates effective document clustering. Cluster labels discovered by document clustering can be incorporated into topic models to extract local top…
Clustering using neural networks has recently demonstrated promising performance in machine learning and computer vision applications. However, the performance of current approaches is limited either by unsupervised learning or their dependence on large set of labeled data samples. In this paper, we propose ClusterNet …
We consider the problem of decentralized clustering and estimation over multi-task networks, where agents infer and track different models of interest. The agents do not know beforehand which model is generating their own data. They also do not know which agents in their neighborhood belong to the same cluster. We prop…
This paper evaluates and validates cluster results using external and internal evaluation methods.
Enhances clustering performance with a novel high-order Laplacian matrix.
Clustering is an essential data mining tool that aims to discover inherent cluster structure in data. For most applications, applying clustering is only appropriate when cluster structure is present. As such, the study of clusterability, which evaluates whether data possesses such structure, is an integral part of clus…
Improves clustering performance by mixing latent representations.
KCoreMotif clusters large networks efficiently by exploiting k-core decomposition and motifs.
Differentiable clustering method using perturbed spanning forests.
We study a variant of the variational autoencoder model (VAE) with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep generative models. We observe that the known problem of over-regularisation that has been shown to arise in regular VAEs also manifests itself i…