arXiv research

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

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

FOSC-X: An extended framework for extracting multiple optimal flat clusterings from hierarchical cluster trees

reval package selects best clustering solutions via stability-based validation.

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…

Three algorithms improve starting solutions for clustering problems.

Plain vanilla K-means clustering has proven to be successful in practice, yet it suffers from outlier sensitivity and may produce highly unbalanced clusters. To mitigate both shortcomings, we formulate a joint outlier detection and clustering problem, which assigns a prescribed number of datapoints to an auxiliary outl…

DMClusts discovers multiple clusterings from multi-view data.

Efficient algorithms find solutions in a rare well-connected cluster at low constraint densities.

This paper certifies cluster assignments from sum-of-norms clustering algorithms.

Proportional centroid clustering aims to fairly group points without prior protected subsets.

Proposes a variational framework for fair clustering.

An explicit solution found for maximizing/minimizing agreement in a 2x2 table.

Graph clustering improved using Boltzmann machine heuristics.

Study connects spectral clustering to maximum margin and level set estimation.

Unified approach for non-stationary and clustered bandits.

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

CARP speeds up convex clustering by 100x and offers better visualization.

ERICA assesses reproducibility in cluster analysis.

While computer and communication technologies have provided effective means to scale up many aspects of education, the submission and grading of assessments such as homework assignments and tests remains a weak link. In this paper, we study the problem of automatically grading the kinds of open response mathematical qu…

New visual quality index for fuzzy clustering.

EAP clusters evolving data, promoting temporal smoothness and automatic cluster tracking.

New solutions to 3D integrability equations using quantum cluster algebras.

The study finds dense clusters of solutions in a simple neural network model, providing bounds for their existence.

Fast accumulation of large amounts of complex data has created a need for more sophisticated statistical methodologies to discover interesting patterns and better extract information from these data. The large scale of the data often results in challenging high-dimensional estimation problems where only a minority of t…

We present a global optimization algorithm for clustering data given the ratio of likelihoods that each pair of data points is in the same cluster or in different clusters. To define a clustering solution in terms of pairwise relationships, a necessary and sufficient condition is that belonging to the same cluster sati…

Spectral clustering has found extensive use in many areas. Most traditional spectral clustering algorithms work in three separate steps: similarity graph construction; continuous labels learning; discretizing the learned labels by k-means clustering. Such common practice has two potential flaws, which may lead to sever…

Paper provides a performance guarantee for spectral clustering.

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

This paper proposes an organized generalization of Newman and Girvan's modularity measure for graph clustering. Optimized via a deterministic annealing scheme, this measure produces topologically ordered graph clusterings that lead to faithful and readable graph representations based on clustering induced graphs. Topog…

Unified 3D R-matrices from quantum cluster algebra.

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

Kernel methods are popular in clustering due to their generality and discriminating power. However, we show that many kernel clustering criteria have density biases theoretically explaining some practically significant artifacts empirically observed in the past. For example, we provide conditions and formally prove the…

Efficient clustering for large datasets using a sampling-based approach.

Model selection in clustering requires (i) to specify a suitable clustering principle and (ii) to control the model order complexity by choosing an appropriate number of clusters depending on the noise level in the data. We advocate an information theoretic perspective where the uncertainty in the measurements quantize…

Tree Index evaluates cluster quality by creating decision trees from data.

FONT clusters patients across health systems with privacy and efficiency.

The paper identifies redundant columns in matrices for feature selection and clustering.

The paper introduces a new method to select high-quality clustering solutions in k-means.

New algorithms improve spectral clustering for finite mixture models.

IMPACC improves consensus clustering for bioinformatics data.

The Allen-Cahn system on manifolds yields multiple phase distributions.

Algorithm identifies intended fairness constraints from expert demonstrations for fair clustering.

Study examines how measurement errors impact clustering algorithms.

Information-maximization clustering learns a probabilistic classifier in an unsupervised manner so that mutual information between feature vectors and cluster assignments is maximized. A notable advantage of this approach is that it only involves continuous optimization of model parameters, which is substantially easie…

Network Lasso clusters sparse graph clusters efficiently.

AUC used as a measure of clustering quality in unsupervised learning.

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…