arXiv research

Paper detects gradual changes in cluster structure using MC fusion.

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…

The paper introduces the concept of a cluster structure to define a joint distribution of the sample size and its exchangeable random partitions. The cluster structure allows the probability distribution of the random partitions of a subset of the sample to be dependent on the sample size, a feature not presented in a …

GCML preserves geometric structure in manifold clustering for diverse data types.

CCMM efficiently solves large-scale convex clustering problems.

CSTS benchmarks time series clustering by evaluating correlation structures.

CCC clusters with controlled spread, outperforming standard methods.

VC-PCR improves prediction by clustering correlated variables.

New method learns DAG structure in clustered data, accounting for local variations.

Proposes a method for multi-view clustering that considers local structures and feature weights.

New clustering method recovers hidden tree structure from data.

Unified framework for clustering and learning causal graphs across subjects.

Clustering is one of the most universal approaches for understanding complex data. A pivotal aspect of clustering analysis is quantitatively comparing clusterings; clustering comparison is the basis for many tasks such as clustering evaluation, consensus clustering, and tracking the temporal evolution of clusters. In p…

Clustering is an essential data mining tool that aims to discover inherent cluster structure in data. As such, the study of clusterability, which evaluates whether data possesses such structure, is an integral part of cluster analysis. Yet, despite their central role in the theory and application of clustering, current…

We propose a novel method for multiple clustering that assumes a co-clustering structure (partitions in both rows and columns of the data matrix) in each view. The new method is applicable to high-dimensional data. It is based on a nonparametric Bayesian approach in which the number of views and the number of feature-/…

New algorithm clusters hyperspectral images at multiple scales.

Cross-manifold clustering is a hard topic and many traditional clustering methods fail because of the cross-manifold structures. In this paper, we propose a Multiple Flat Projections Clustering (MFPC) to deal with cross-manifold clustering problems. In our MFPC, the given samples are projected into multiple subspaces t…

This paper presents a new approach to non-parametric cluster analysis called Adaptive Weights Clustering (AWC). The idea is to identify the clustering structure by checking at different points and for different scales on departure from local homogeneity. The proposed procedure describes the clustering structure in term…

New spectral clustering method handles discrete covariates for better community detection.

A clustering method for multivariate populations with similar dependence structures.

This paper proposes a spectral clustering algorithm for hyperbolic spaces, improving efficiency over Euclidean methods.

Proves log-concavity of cluster algebra coefficients for type $A_n$.

Adapts manifold structure for better clustering performance.

We present a structural clustering algorithm for large-scale datasets of small labeled graphs, utilizing a frequent subgraph sampling strategy. A set of representatives provides an intuitive description of each cluster, supports the clustering process, and helps to interpret the clustering results. The projection-based…

Competency questions help experts select best clustering for energy data.

A method for clustering small datasets in high dimensions using random projections.

Geometric framework links clustering accuracy to structural recovery.

Proposes a graph learning framework for clustering and semi-supervised classification.

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

ERICA assesses reproducibility in cluster analysis.

Multiple clustering aims at discovering diverse ways of organizing data into clusters. Despite the progress made, it's still a challenge for users to analyze and understand the distinctive structure of each output clustering. To ease this process, we consider diverse clusterings embedded in different subspaces, and ana…

A hierarchical clustering algorithm for data clouds without structure assumptions.

We propose a simple and efficient time-series clustering framework particularly suited for low Signal-to-Noise Ratio (SNR), by simultaneous smoothing and dimensionality reduction aimed at preserving clustering information. We extend the sparse K-means algorithm by incorporating structured sparsity, and use it to exploi…

Clustering is a fundamental task in data analysis. Recently, deep clustering, which derives inspiration primarily from deep learning approaches, achieves state-of-the-art performance and has attracted considerable attention. Current deep clustering methods usually boost the clustering results by means of the powerful r…

DPGIIL clusters structural anomalies using transmissibility functions with deep learning and Dirichlet process.

New spectral clustering method using LASSO regularization for robust graph partitioning.

We define transit clusters to simplify causal diagrams and preserve their essential properties.

Optimal algorithm for latent bandits with cluster structure reduces regret to nearly optimal.

This study evaluates clustering algorithms on high-dimensional data.

Proposes HypCSE for enhanced hierarchical clustering.

Paper clusters microstructure measures for better stock return prediction.

A fundamental property of complex networks is the tendency for edges to cluster. The extent of the clustering is typically quantified by the clustering coefficient, which is the probability that a length-2 path is closed, i.e., induces a triangle in the network. However, higher-order cliques beyond triangles are crucia…

Bayesian methods detect clusters in noisy data more reliably.

With the recent popularity of graphical clustering methods, there has been an increased focus on the information between samples. We show how learning cluster structure using edge features naturally and simultaneously determines the most likely number of clusters and addresses data scale issues. These results are parti…

DBS uses swarm intelligence to cluster data without needing a global objective function.

ElbowSig assesses clustering structure at multiple scales.

In a standard cluster analysis, such as k-means, in addition to clusters locations and distances between them, it's important to know if they are connected or well separated from each other. The main focus of this paper is discovering the relations between the resulting clusters. We propose a new method which is based …

t-NEB clusters high-dimensional data hierarchically with density paths.