arXiv research

MAS scores cluster size consistency from points, robust to label changes.

New algorithm improves hypergraph clustering for unbalanced communities.

Enhanced spectral clustering for geometric graphs improves clustering accuracy.

Paper proposes SLINK clustering for nonparametric data sequences with improved consistency.

Study on Dirichlet process mixtures for clustering consistency.

Although consistency is a minimum requirement of any estimator, little is known about consistency of the mean partition approach in consensus clustering. This contribution studies the asymptotic behavior of mean partitions. We show that under normal assumptions, the mean partition approach is consistent and asymptotic …

CLASSIX is a fast and explainable clustering method that sorts data and merges groups.

Algorithm identifies fractal system's scaling exponents in high dimensions.

New algorithms improve community detection in network data with strong consistency.

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…

Develops a framework for consistent clustering algorithm benchmarking.

Cluster jackknife improves inference for staggered DID methods.

In the context of clustering, we consider a generative model in a Euclidean ambient space with clusters of different shapes, dimensions, sizes and densities. In an asymptotic setting where the number of points becomes large, we obtain theoretical guaranties for a few emblematic methods based on pairwise distances: a si…

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 …

Most generative models for clustering implicitly assume that the number of data points in each cluster grows linearly with the total number of data points. Finite mixture models, Dirichlet process mixture models, and Pitman--Yor process mixture models make this assumption, as do all other infinitely exchangeable cluste…

This paper establishes the consistency of a family of graph-cut-based algorithms for clustering of data clouds. We consider point clouds obtained as samples of a ground-truth measure. We investigate approaches to clustering based on minimizing objective functionals defined on proximity graphs of the given sample. Our f…

New concept of mixture complexity helps detect gradual clustering changes.

Text clustering method replaces centroids with summaries for interpretability and scalability.

Formula found for minimum ARI between clusterings of fixed sizes.

New algorithm achieves strong consistency in binary non-uniform hypergraph classification.

New SDP algorithm recovers large clusters in SBM with small clusters of any size.

Kleinberg introduced three natural clustering properties, or axioms, and showed they cannot be simultaneously satisfied by any clustering algorithm. We present a new clustering property, Monotonic Consistency, which avoids the well-known problematic behaviour of Kleinberg's Consistency axiom, and the impossibility resu…

This paper discusses about an R package that implements the Pattern Sequence based Forecasting (PSF) algorithm, which was developed for univariate time series forecasting. This algorithm has been successfully applied to many different fields. The PSF algorithm consists of two major parts: clustering and prediction. The…

An intrinsic problem of classifiers based on machine learning (ML) methods is that their learning time grows as the size and complexity of the training dataset increases. For this reason, it is important to have efficient computational methods and algorithms that can be applied on large datasets, such that it is still …

New algorithm IAC recovers hidden communities in labeled SBM with optimal performance.

Spectral clustering methods which are frequently used in clustering and community detection applications are sensitive to the specific graph constructions particularly when imbalanced clusters are present. We show that ratio cut (RCut) or normalized cut (NCut) objectives are not tailored to imbalanced cluster sizes sin…

Study of hill-climbing clustering methods and their consistency.

We develop a coreset for robust geometric median, reducing size dependency on outliers.

PNN-smoothing improves $k$-means clustering by merging subsets' clusterings.

As Machine Learning (ML) applications increase in data size and model complexity, practitioners turn to distributed clusters to satisfy the increased computational and memory demands. Unfortunately, effective use of clusters for ML requires considerable expertise in writing distributed code, while highly-abstracted fra…

Most generative models for clustering implicitly assume that the number of data points in each cluster grows linearly with the total number of data points. Finite mixture models, Dirichlet process mixture models, and Pitman--Yor process mixture models make this assumption, as do all other infinitely exchangeable cluste…

This work refines grid size selection for non-interactive private $K$-means clustering.

In high-dimension, low-sample size (HDLSS) data, it is not always true that closeness of two objects reflects a hidden cluster structure. We point out the important fact that it is not the closeness, but the "values" of distance that contain information of the cluster structure in high-dimensional space. Based on this …

Adapts DR objectives for both sample and feature size reduction.

Clustering consists of grouping together samples giving their similar properties. The problem of modeling simultaneously groups of samples and features is known as Co-Clustering. This paper introduces ROCCO - a Robust Continuous Co-Clustering algorithm. ROCCO is a scalable, hyperparameter-free, easy and ready to use al…

Proposes a method for multi-view clustering that integrates consistent and complementary graph regularizers.

Convex clustering can only learn convex clusters, with significant gaps between clusters.

A new method clusters mixed-type data efficiently.

Proposes a differentiable hypergeometric distribution for learning group importance.

The study investigates the consistency of $k$-means clustering under finite expectation assumptions.

The paper analyzes the generalization performance of spectral clustering algorithms and proposes new methods to improve their effectiveness.

Clustering is a central approach for unsupervised learning. After clustering is applied, the most fundamental analysis is to quantitatively compare clusterings. Such comparisons are crucial for the evaluation of clustering methods as well as other tasks such as consensus clustering. It is often argued that, in order to…

Estimates statistical power for cluster analysis in biomedical research.

This study evaluates cluster search algorithms using Gaussian mixture models.

Resolving a conjecture of Abbe, Bandeira and Hall, the authors have recently shown that the semidefinite programming (SDP) relaxation of the maximum likelihood estimator achieves the sharp threshold for exactly recovering the community structure under the binary stochastic block model of two equal-sized clusters. The s…

Intuitive clustering algorithm balances cluster size and cohesion.

New method for clustering tasks with heterogeneous data.

A new $k$-means method improves clustering accuracy and robustness.