arXiv research

This paper reviews weighted clustering ensemble methods.

New method clusters hypergraphs using weighted random walks and Laplacians.

A new fuzzy k-means algorithm for high-dimensional data with variable feature weights.

Proposes a weighted conformal approach for cluster label uncertainty.

New method clusters weighted directed networks using motifs.

As a model problem for clustering, we consider the densest k-disjoint-clique problem of partitioning a weighted complete graph into k disjoint subgraphs such that the sum of the densities of these subgraphs is maximized. We establish that such subgraphs can be recovered from the solution of a particular semidefinite re…

Proposes a new model for clustering multiplex networks with compositional data.

Exploiting different representations, or views, of the same object for better clustering has become very popular these days, which is conventionally called multi-view clustering. Generally, it is essential to measure the importance of each individual view, due to some noises, or inherent capacities in description. Many…

DMFAW improves multi-view clustering with adaptive weights and feature selection.

Proposes WM-NMF for better multi-view clustering.

New method clusters matrix-valued data by latent variables.

Robust feature-weighted jump models for time-dependent clustering

We introduce a principled method for the signed clustering problem, where the goal is to partition a graph whose edge weights take both positive and negative values, such that edges within the same cluster are mostly positive, while edges spanning across clusters are mostly negative. Our method relies on a graph-based …

Study of $g$-vector cones in cluster algebras from weighted orbifolds.

Improved accuracy in machine learning with Cross-Cluster Weighted Forests.

The classical $k$-means algorithm for partitioning $n$ points in $\mathbb{R}^d$ into $k$ clusters is one of the most popular and widely spread clustering methods. The need to respect prescribed lower bounds on the cluster sizes has been observed in many scientific and business applications. In this paper, we present an…

New method embeds phylogenetic trees for clustering, recovering evolutionary relationships.

A new model clusters network nodes based on relative edge weights.

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

We study the use of power weighted shortest path distance functions for clustering high dimensional Euclidean data, under the assumption that the data is drawn from a collection of disjoint low dimensional manifolds. We argue, theoretically and experimentally, that this leads to higher clustering accuracy. We also pres…

Develops a new random forest method for clustered data with improved prediction and inference.

We define a class of Euclidean distances on weighted graphs, enabling to perform thermodynamic soft graph clustering. The class can be constructed form the "raw coordinates" encountered in spectral clustering, and can be extended by means of higher-dimensional embeddings (Schoenberg transformations). Geographical flow …

A new method for clustering functional data outperforms existing methods.

A family of parsimonious Gaussian cluster-weighted models is presented. This family concerns a multivariate extension to cluster-weighted modelling that can account for correlations between multivariate responses. Parsimony is attained by constraining parts of an eigen-decomposition imposed on the component covariance …

New concept of mixture complexity helps detect gradual clustering changes.

Data clustering has received a lot of attention and numerous methods, algorithms and software packages are available. Among these techniques, parametric finite-mixture models play a central role due to their interesting mathematical properties and to the existence of maximum-likelihood estimators based on expectation-m…

The paper proves an infinite double bubble theorem in higher dimensions.

The clustering ensemble technique aims to combine multiple clusterings into a probably better and more robust clustering and has been receiving an increasing attention in recent years. There are mainly two aspects of limitations in the existing clustering ensemble approaches. Firstly, many approaches lack the ability t…

We formulate weighted graph clustering as a prediction problem: given a subset of edge weights we analyze the ability of graph clustering to predict the remaining edge weights. This formulation enables practical and theoretical comparison of different approaches to graph clustering as well as comparison of graph cluste…

We consider a decomposition method for compressive streaming data in the context of online compressive Robust Principle Component Analysis (RPCA). The proposed decomposition solves an $n$-$\ell_1$ cluster-weighted minimization to decompose a sequence of frames (or vectors), into sparse and low-rank components, from com…

clusterBMA combines clustering results from multiple models using Bayesian model averaging.

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

A new metric framework for weighted projective spaces improves clustering and analysis.

Local graph clustering improves with noisy labels, enhancing accuracy and performance.

A new clustering method for functional data using skewed distributions.

New bounds for convex clustering under graph connectivity.

Machine learning uncovers hidden patterns in Calabi-Yau hypersurfaces.

Clustering is one of the major roles in data mining that is widely application in pattern recognition and image segmentation. Fuzzy C-means (FCM) is the most used clustering algorithm that proven efficient, fast and easy to implement, however, FCM uses the Euclidean distance that often leads to clustering errors, espec…

Improved spectral clustering for community detection in networks.

In mixture model-based clustering applications, it is common to fit several models from a family and report clustering results from only the `best' one. In such circumstances, selection of this best model is achieved using a model selection criterion, most often the Bayesian information criterion. Rather than throw awa…

New algorithm clusters trajectories from multiple Markov chains with near-optimal error.

We describe $k$-MLE, a fast and efficient local search algorithm for learning finite statistical mixtures of exponential families such as Gaussian mixture models. Mixture models are traditionally learned using the expectation-maximization (EM) soft clustering technique that monotonically increases the incomplete (expec…

Multi-view clustering is an important yet challenging task due to the difficulty of integrating the information from multiple representations. Most existing multi-view clustering methods explore the heterogeneous information in the space where the data points lie. Such common practice may cause significant information …

A feature-weighted mean shift algorithm improves clustering in high-dimensional data.

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…

CW-EDMD improves prediction accuracy by learning local Koopman models for different state-space regions.

Distributional (or distribution-valued) data are a new type of data arising from several sources and are considered as realizations of distributional variables. A new set of fuzzy c-means algorithms for data described by distributional variables is proposed. The algorithms use the $L2$ Wasserstein distance between dist…

HSACC improves multi-view clustering of incomplete data.