A novel clustering method uses torque balance to group objects.
problem Grouping similar objects in various scientific fields.
method Inspired by gravitational interactions, a parameter-free clustering algorithm based on mass and distance.
result The algorithm effectively clusters objects regardless of their shape, size, or density.
BalLOT uses optimal transport for balanced k-means clustering.
problem Balanced k-means clustering of data. method BalLOT is an optimal transport approach to alternating minimization.
result BalLOT provides theoretical guarantees for exact and partial recoveries of planted clusters.
New model for fair clustering ensures balanced representation of protected attributes.
problem Ensuring fair representation in clustering for protected attributes.
method Model-based formulation of fair clustering, balancing protected attributes across clusters.
result Demonstrates improved fairness in clustering through a new model.
New method finds balanced clusters in graphs using auxiliary information.
problem Finding balanced clusters in graphs with population-level constraints.
method Proposes individual-level balancing constraint and develops spectral clustering algorithms.
result Establishes first statistical consistency result for constrained spectral clustering.
A new algorithm balances fairness in clustering to avoid discrimination.
problem Clustering data can unfairly discriminate against different demographic groups.
method Designing a stochastic alternating balance fair k-means algorithm (SAfairKM) that alternates between k-means updates and group swap updates.
result The algorithm efficiently constructs well-spread and high-quality Pareto fronts on synthetic and real datasets.
Intuitive clustering algorithm balances cluster size and cohesion.
problem Cluster definition and selection in data analysis.
method Nearest neighbours equilibrium condition for clustering.
result High-quality clustering solutions compared to benchmarks.
Data balancing reduces variance in machine learning models.
problem Reduction of variance in machine learning models.
method Non-asymptotic statistical bound and eigenvalue decay of Markov operators.
result Data balancing across modalities and sources reduces variance.
Spectral Clustering as a relaxation of the normalized/ratio cut has become one of the standard graph-based clustering methods. Existing methods for the computation of multiple clusters, corresponding to a balanced k-cut of the graph, are either based on greedy techniques or heuristics which have weak connection to th…
This article explores and analyzes the unsupervised clustering of large partially observed graphs. We propose a scalable and provable randomized framework for clustering graphs generated from the stochastic block model. The clustering is first applied to a sub-matrix of the graph's adjacency matrix associated with a re…
The classical k-means algorithm for partitioning n points in Rd 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…
PET-TURTLE improves clustering accuracy for imbalanced data.
problem Imbalanced data causes clustering errors.
method Generalizes cost function and introduces sparse logits.
result PET-TURTLE enhances overall clustering accuracy for imbalanced data.
In this paper, we propose a deep reinforcement learning (DRL) based mobility load balancing (MLB) algorithm along with a two-layer architecture to solve the large-scale load balancing problem for ultra-dense networks (UDNs). Our contribution is three-fold. First, this work proposes a two-layer architecture to solve the…
CwA optimizes search performance by jointly learning a balanced database partition and a neural probing function.
problem Suboptimal search performance due to mismatched database and query distributions.
method CwA jointly learns a balanced database partition and a neural probing function to optimize search performance directly for the query distribution.
result CwA achieves up to 4.7x throughput over state-of-the-art methods at equal recall.
Novel GNN method for semi-supervised clustering of signed networks.
problem Lack of effective node embeddings for signed network clustering.
method SSSNET: Probabilistic balanced normalized cut loss for GNN.
result SSSNET achieves comparable or better results than state-of-the-art methods.
The paper introduces group-representative clustering to ensure fair representation of different groups in clusters.
problem Ensuring fair representation of different groups in clusters.
method Developed a new clustering approach called group-representative clustering, which parallels fairness notions in classification.
result Presented approximation algorithms for group representative k-median clustering and evaluated on real-world data. In this work we introduce two novel deterministic annealing based clustering algorithms to address the problem of Edge Controller Placement (ECP) in wireless edge networks. These networks lie at the core of the fifth generation (5G) wireless systems and beyond. These algorithms, ECP-LL and ECP-LB, address the dominant …
New algorithm for multiway spectral clustering on Grassmann manifolds.
problem Efficiently computing multiple eigenvectors of a nonlinear graph Laplacian.
method Direct multiway spectral clustering in p-norm, reformulated as minimization on Grassmann manifold. result Monotonic decrease of balanced graph cuts leads to optimal solutions.
Unsupervised domain adaptation seeks to learn an invariant and discriminative representation for an unlabeled target domain by leveraging the information of a labeled source dataset. We propose to improve the discriminative ability of the target domain representation by simultaneously learning tightly clustered target …
Agglomerative hierarchical clustering can be implemented with several strategies that differ in the way elements of a collection are grouped together to build a hierarchy of clusters. Here we introduce versatile linkage, a new infinite system of agglomerative hierarchical clustering strategies based on generalized mean…
SurvMixClust clusters survival data and predicts individual survival curves.
problem Integrating clustering into survival analysis for precision medicine.
method SurvMixClust learns latent representations for clustering and predicts survival functions using a mixture of non-parametric experts.
result SurvMixClust creates balanced clusters with distinct survival curves, outperforming clustering baselines and competing with non-clustering models in predictive accuracy.
DPMM-CFL clusters clients for federated learning without fixed K, improving performance.
problem Improving federated learning performance under non-IID client heterogeneity.
method DPMM-CFL uses a Dirichlet Process Mixture Model to infer both cluster number and client assignments.
result DPMM-CFL optimizes per-cluster federated objectives and jointly infers cluster number and assignments.
BICauseTree improves causal effect estimation by identifying clusters and balancing treatment allocation.
problem Improving interpretability and transparency in causal effect models from observational data.
method Hierarchical bias-driven stratification using decision trees with a customized objective function.
result BICauseTree provides interpretable causal effect estimation and is comparable to existing methods.
State-of-the-art subspace clustering methods are based on expressing each data point as a linear combination of other data points while regularizing the matrix of coefficients with ℓ1, ℓ2 or nuclear norms. ℓ1 regularization is guaranteed to give a subspace-preserving affinity (i.e., there are no conne…
FCA improves fair clustering by optimizing utility and fairness.
problem Balancing fairness and utility in clustering.
method FCA alternates between aligning data and optimizing cluster centers in an aligned space.
result FCA achieves a superior trade-off between fairness and utility.
Graph clustering groups entities -- the vertices of a graph -- based on their similarity, typically using a complex distance function over a large number of features. Successful integration of clustering approaches in automated decision-support systems hinges on the interpretability of the resulting clusters. This pape…
Study examines how cluster number affects short-text clustering, introducing a stability metric.
problem Challenges in finding meaningful clusters in short-text data.
method Introduces a stability metric to determine cluster robustness and visualizes cluster subdivisions.
result Choosing a cluster number involves balancing informativeness and complexity, not seeking a single 'optimal' solution.
Improved clustering accuracy with disentangled latent code representation.
problem Improving k-Means clustering performance.
method Optimizing the entanglement of autoencoder latent code representation using soft nearest neighbor loss with annealing temperature.
result 96.2% test clustering accuracy on MNIST, 85.6% on Fashion-MNIST, and 79.2% on EMNIST Balanced datasets.
Convex clustering, a convex relaxation of k-means clustering and hierarchical clustering, has drawn recent attentions since it nicely addresses the instability issue of traditional nonconvex clustering methods. Although its computational and statistical properties have been recently studied, the performance of convex c…
Motivated by social balance theory, we develop a theory of link classification in signed networks using the correlation clustering index as measure of label regularity. We derive learning bounds in terms of correlation clustering within three fundamental transductive learning settings: online, batch and active. Our mai…
A new hybrid fuzzy-crisp clustering algorithm addresses imbalanced cluster sizes.
problem Imbalanced influence in fuzzy c-means clustering for large vs. small clusters.
method Hybrid fuzzy-crisp algorithm combining linear and quadratic membership functions, setting exact zero membership for sufficiently distant points.
result The hybrid algorithm outperforms conventional fuzzy and crisp clustering methods on imbalanced datasets.
The paper clusters hypergraphs to find diverse and experienced groups based on past experiences.
problem Finding diverse and experienced groups with respect to past experiences.
method Regularized edge-based hypergraph clustering objective with a 2-approximation algorithm.
result Demonstrates an efficient 2-approximation algorithm for clustering hypergraphs.
Regularized spectral methods improve clustering in signed graphs, especially for sparse data.
problem Clustering signed graphs with positive and negative edges.
method Developed regularized versions of SPONGE and Signed Laplacian methods for clustering signed graphs, especially for sparse data.
result Theoretical guarantees and empirical performance improvements for clustering signed graphs, especially in sparse regimes.
New algorithm improves hypergraph clustering for unbalanced communities.
problem Hypergraph clustering with unbalanced community sizes and outliers.
method CRTMLE algorithm using convex relaxation of truncated MLE.
result Achieves optimal recovery guarantees for unbalanced community sizes.
Multi-view clustering has received much attention recently. Most of the existing multi-view clustering methods only focus on one-sided clustering. As the co-occurring data elements involve the counts of sample-feature co-occurrences, it is more efficient to conduct two-sided clustering along the samples and features si…
MASC balances dataset representation using affinity clustering and distribution discrepancies.
problem Representation bias in datasets due to group imbalance.
method MASC uses affinity clustering and pairwise distribution discrepancies to balance non-protected and protected groups.
result MASC effectively debiases target datasets, comparable to existing methods.
The study investigates the consistency of k-means clustering under finite expectation assumptions.
problem Consistency of k-means clustering under finite expectation assumptions. method Investigates the conditions under which k-means clustering is consistent, considering finite expectation instead of finite variance. result Inconsistency can arise due to extreme cluster imbalance, leading to some clusters having few points.
The paper optimizes portfolios using clustering and Sharpe ratio-based optimization.
problem Optimizing portfolio performance in financial modeling.
method Combines K-Means clustering for asset segmentation and Sharpe ratio-based optimization.
result Optimized portfolios outperform traditional equal-weighted benchmarks.
ExKMC improves explainable k-means clustering by balancing accuracy and simplicity.
problem Limited explainable methods for unsupervised learning.
method Develops ExKMC, a new algorithm that uses a decision tree with k′ leaves to explain k-means clustering, trading explainability for accuracy. result ExKMC produces a low-cost clustering that outperforms existing methods.
We introduce a principled and theoretically sound spectral method for k-way clustering in signed graphs, where the affinity measure between nodes takes either positive or negative values. Our approach is motivated by social balance theory, where the task of clustering aims to decompose the network into disjoint group…
Most existing approaches address multi-view subspace clustering problem by constructing the affinity matrix on each view separately and afterwards propose how to extend spectral clustering algorithm to handle multi-view data. This paper presents an approach to multi-view subspace clustering that learns a joint subspace…
A novel clustering method using a subset of data for better performance.
problem Improving clustering efficiency with limited data.
method Proposes a sampling subset approach for variance-based k-clustering.
result The method achieves competitive results with only 7% of the dataset, outperforming existing methods in 80% of tasks.
Study evaluates clustering methods for Google Trends data.
problem Clustering high-dimensional, noisy time series data.
method Symbolic Aggregate Approximation (SAX), Enhanced SAX (eSAX), and Topological Data Analysis (TDA).
result TDA provides more balanced and meaningful groupings than SAX and eSAX.
Assignment methods are at the heart of many algorithms for unsupervised learning and clustering - in particular, the well-known K-means and Expectation-Maximization (EM) algorithms. In this work, we study several different methods of assignment, including the "hard" assignments used by K-means and the ?soft' assignment…
A new algorithm removes unexpected correlations in biased data for better clustering.
problem Clustering with selection bias in data.
method Decorrelation regularized K-Means (DCKM) algorithm.
result DCKM achieves significant performance gains on real-world datasets.
In practical machine learning systems, graph based data representation has been widely used in various learning paradigms, ranging from unsupervised clustering to supervised classification. Besides those applications with natural graph or network structure data, such as social network analysis and relational learning, …
Consistent spectral clustering with fairness constraints on representation graphs.
problem Finding balanced clusters in similarity graphs with fairness constraints.
method Developed variants of unnormalized and normalized spectral clustering for fair planted partitions.
result Consistency results for constrained spectral clustering under fair planted partitions.
EKM addresses imbalanced data clustering by repelling centroids in large clusters.
problem Imbalanced data leads to biased clustering of large clusters.
method EKM introduces a novel centroid repulsion mechanism based on the Boltzmann operator.
result EKM outperforms benchmark algorithms on imbalanced data.
Clustering is an essential technique for discovering patterns in data. The steady increase in amount and complexity of data over the years led to improvements and development of new clustering algorithms. However, algorithms that can cluster data with mixed variable types (continuous and categorical) remain limited, de…