arXiv research

Improved SSC clustering with reduced computation time and accuracy.

Paper explores limits of high-order clustering with planted structures.

New computational methods improve clustering of objects.

Ball k-means reduces point-centroid distance computations for faster k-means clustering.

Clustered attention improves transformer efficiency for large sequences.

IMPACC improves consensus clustering for bioinformatics data.

New computational lower bounds for clustering and related problems.

Proposes new random models for fuzzy clustering similarity measures.

A new parallel clustering method improves speed and accuracy for single cell transcriptomic data.

A new clustering algorithm reduces density peaks clustering's computational complexity.

The cost of computing the spectrum of Laplacian matrices hinders the application of spectral clustering to large data sets. While approximations recover computational tractability, they can potentially affect clustering performance. This paper proposes a practical approach to learn spectral clustering based on adaptive…

Spectral clustering is a widely studied problem, yet its complexity is prohibitive for dynamic graphs of even modest size. We claim that it is possible to reuse information of past cluster assignments to expedite computation. Our approach builds on a recent idea of sidestepping the main bottleneck of spectral clusterin…

New clustering method using point-set kernel measures similarity.

CDL index improves clustering validation for non-convex data.

New criterion assesses cluster separability for validation.

Spectral clustering is one of the most effective clustering approaches that capture hidden cluster structures in the data. However, it does not scale well to large-scale problems due to its quadratic complexity in constructing similarity graphs and computing subsequent eigendecomposition. Although a number of methods h…

Paper presents a faster method for computing cost of equity and performing comparable company analysis.

New method reduces clustering time and improves accuracy.

The smallest eigenvalues and the associated eigenvectors (i.e., eigenpairs) of a graph Laplacian matrix have been widely used in spectral clustering and community detection. However, in real-life applications the number of clusters or communities (say, $K$) is generally unknown a-priori. Consequently, the majority of t…

Spectral clustering approaches have led to well-accepted algorithms for finding accurate clusters in a given dataset. However, their application to large-scale datasets has been hindered by computational complexity of eigenvalue decompositions. Several algorithms have been proposed in the recent past to accelerate spec…

A new algorithm speeds up convex clustering.

This article explores and analyzes the unsupervised clustering of large partially observed graphs. We propose a scalable and provable randomized framework for clustering graphs generated from the stochastic block model. The clustering is first applied to a sub-matrix of the graph's adjacency matrix associated with a re…

Efficiently clusters survival curves without computationally intensive resampling.

Polynomial-time algorithm finds planted hypercube vectors in Gaussian mixtures.

Graph pooling method uses GNN to cluster graphs efficiently.

DCMAP optimizes clustering in Bayesian Networks with dependent costs.

Local SGD outperforms conventional methods in LLM training.

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

Paper proposes efficient methods for high-order clustering in tensor block models.

In a variety of research areas, the weighted bag of vectors and the histogram are widely used descriptors for complex objects. Both can be expressed as discrete distributions. D2-clustering pursues the minimum total within-cluster variation for a set of discrete distributions subject to the Kantorovich-Wasserstein metr…

PASCO speeds up graph clustering for large graphs.

We construct geometric realization for non-exceptional mutation-finite cluster algebras by extending the theory of Fomin and Thurston to skew-symmetrizable case. Cluster variables for these algebras are renormalized lambda lengths on certain hyperbolic orbifolds. We also compute growth rate of these cluster algebras, p…

Efficient algorithms for clustered Lasso and OSCAR reduce computational costs.

We try to give a cluster algebraic interpretation of complex volume of knots. We construct the R-operator from the cluster mutations, and we show that it is regarded as a hyperbolic octahedron. The cluster variables are interpreted as edge parameters used by Zickert in computing complex volume.

Develops a new tensor model for clustering with degree correction.

We give a method to compute presentations of saturated cluster modular groups. Using this, we obtain finite presentations of the saturated cluster modular groups of finite mutation type $X_6$ and $X_7$. We verify that the cluster modular groups of finite mutation type $\widetilde{E}_6$, $\widetilde{E}_7$, $\widetilde{E…

New spectral clustering method for graphs with uneven node degrees.

A new k-means algorithm using cover trees accelerates clustering.

In this paper, we study different discrete data clustering methods, which use the Model-Based Clustering (MBC) framework with the Multinomial distribution. Our study comprises several relevant issues, such as initialization, model estimation and model selection. Additionally, we propose a novel MBC method by efficientl…

Researchers compute A-polynomials of manifolds using symplectic properties and cluster algebras.

We develop an iterative subsampling approach to improve the computational efficiency of our previous work on solution path clustering (SPC). The SPC method achieves clustering by concave regularization on the pairwise distances between cluster centers. This clustering method has the important capability to recognize no…

Network Lasso clusters sparse graph clusters efficiently.

A new framework for spectral clustering over distributed data with minimal communication.

Global optimization approach for MAP clustering under Gaussian mixtures.

Cluster analysis is a fundamental tool for pattern discovery of complex heterogeneous data. Prevalent clustering methods mainly focus on vector or matrix-variate data and are not applicable to general-order tensors, which arise frequently in modern scientific and business applications. Moreover, there is a gap between …

Develops clustering methods based on likelihood and convergence proved.

Develops a hybrid MtFA approach for high-dimensional data clustering.

We propose a framework for Semi-Supervised Active Clustering framework (SSAC), where the learner is allowed to interact with a domain expert, asking whether two given instances belong to the same cluster or not. We study the query and computational complexity of clustering in this framework. We consider a setting where…