arXiv research

This paper introduces a persistence metric to compare clustering solutions with different numbers of clusters.

New indices for determining cluster compactness and separability.

In this paper we introduce three methods for re-scaling data sets aiming at improving the likelihood of clustering validity indexes to return the true number of spherical Gaussian clusters with additional noise features. Our method obtains feature re-scaling factors taking into account the structure of a given data set…

Paper proposes a new black-box adversarial attack for clustering models.

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-/…

A new CVI called DSI evaluates clustering results without true labels.

We conduct cluster analysis on a class of locally asymptotically self-similar stochastic processes, which includes multifractional Brownian motion as a representative. When the true number of clusters is supposed to be known, a new covariance-based dissimilarity measure is introduced, from which we obtain the approxima…

A new KM clustering algorithm reduces error and scales to large datasets.

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…

Study on posterior distribution for cluster number in DPMM models.

There has been a surge in the number of large and flat data sets - data sets containing a large number of features and a relatively small number of observations - due to the growing ability to collect and store information in medical research and other fields. Hierarchical clustering is a widely used clustering tool. I…

A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters. To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. The possibly low-dimensional latent mixture model allow…

A mixture of Gaussians fit to a single curved or heavy-tailed cluster will report that the data contains many clusters. To produce more appropriate clusterings, we introduce a model which warps a latent mixture of Gaussians to produce nonparametric cluster shapes. The possibly low-dimensional latent mixture model allow…

This paper presents a neural network-based end-to-end clustering framework. We design a novel strategy to utilize the contrastive criteria for pushing data-forming clusters directly from raw data, in addition to learning a feature embedding suitable for such clustering. The network is trained with weak labels, specific…

The paper develops a MIP for MLR and proves convergence of estimates under certain conditions.

Semi-supervised clustering is the task of clustering data points into clusters where only a fraction of the points are labelled. The true number of clusters in the data is often unknown and most models require this parameter as an input. Dirichlet process mixture models are appealing as they can infer the number of clu…

Study reveals structure of local minima in GMMs, identifying key cluster centers.

We consider the problem of community detection or clustering in the labeled Stochastic Block Model (LSBM) with a finite number $K$ of clusters of sizes linearly growing with the global population of items $n$. Every pair of items is labeled independently at random, and label $\ell$ appears with probability $p(i,j,\ell)…

New method estimates number of clusters robustly in noisy data.

Here, we propose a clustering technique for general clustering problems including those that have non-convex clusters. For a given desired number of clusters $K$, we use three stages to find a clustering. The first stage uses a hybrid clustering technique to produce a series of clusterings of various sizes (randomly se…

Exemplar-based clustering methods have been shown to produce state-of-the-art results on a number of synthetic and real-world clustering problems. They are appealing because they offer computational benefits over latent-mean models and can handle arbitrary pairwise similarity measures between data points. However, when…

Minimax linkage improves clustering interpretability by minimizing maximum distance to prototypes.

Flexible model captures varying scales in data clusters.

CRAD clusters data with robust depth-based dissimilarity, outperforming existing methods.

Proposes a method to improve class-conditional conformal prediction for many classes.

Neural clustering learns time series affinity from statistical features.

New method uses dendrograms for better mixture model selection and clustering.

The paper analyzes $k$-means clustering for missing data, proving statistical guarantees under MCAR.

New index improves anomaly detection in correlated time series data.

Robust feature-weighted jump models for time-dependent clustering

Algorithms based on spectral graph cut objectives such as normalized cuts, ratio cuts and ratio association have become popular in recent years because they are widely applicable and simple to implement via standard eigenvector computations. Despite strong performance for a number of clustering tasks, spectral graph cu…

New insights into spurious local minima in k-means clustering.

We develop coreset techniques for noisy clustering with provable guarantees.

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 …

In this paper, we initiate a rigorous theoretical study of clustering with noisy queries (or a faulty oracle). Given a set of $n$ elements, our goal is to recover the true clustering by asking minimum number of pairwise queries to an oracle. Oracle can answer queries of the form : "do elements $u$ and $v$ belong to the…

CDL index improves clustering validation for non-convex data.

This paper studies ordered weighted L1 (OWL) norm regularization for sparse estimation problems with strongly correlated variables. We prove sufficient conditions for clustering based on the correlation/colinearity of variables using the OWL norm, of which the so-called OSCAR is a particular case. Our results extend pr…

This paper evaluates and validates cluster results using external and internal evaluation methods.

IDM learns clustered representations for complex FELs.

Most density-based clustering methods largely rely on how well the underlying density is estimated. However, density estimation itself is also a challenging problem, especially the determination of the kernel bandwidth. A large bandwidth could lead to the over-smoothed density estimation in which the number of density …

This paper improves robust cluster enumeration for RES data.

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

A framework clusters vehicle motion trajectories efficiently.

The study optimizes bandwidth for nonparametric modal clustering.

Develops a measure for checking cluster quality using multinomial distribution.

A new Wasserstein $K$-means method for clustering probability distributions.

VICatMix clusters categorical biomedical data efficiently and selects relevant variables.

Method selects number of communities in weighted networks.