Probabilistic fair clustering tackles uncertain group membership.
problem Fair clustering with imperfect group membership.
method Probabilistic algorithms for metric graphs and metric membership.
result Approximation ratio guarantees for fair clustering.
TMM improves clustering of temporal data, outperforming existing models.
problem Capturing temporal evolution of clusters in time-driven data.
method Temporal Multinomial Mixture (TMM) model that optimizes feature co-occurrences and temporal smoothness.
result TMM outperforms other clustering models in instance-oriented temporal data.
Unified framework for differentiable graph partitioning with probabilistic cuts.
problem Lack of general guarantees and principled gradients in prior probabilistic relaxations of graph cuts.
method Unified probabilistic framework covering a wide class of cuts, including Normalized Cut, with tight analytic upper bounds.
result Rigorous, numerically stable foundation for scalable, differentiable graph partitioning.
New method for probabilistic clustering using matrix norm couplings.
problem Probabilistic clustering of discrete random variables.
method Local information theoretic approach with convex maximization problem solved via gradient ascent and alternating maximization.
result Our approach is competitive with existing techniques and worthy of further investigation.
DPSOM combines self-organizing maps with deep learning for better data clustering.
problem Improving clustering performance in complex data.
method Integrates self-organizing maps with probabilistic clustering using a VAE.
result DPSOM outperforms current deep clustering methods in various applications.
In this paper we formulate in general terms an approach to prove strong consistency of the Empirical Risk Minimisation inductive principle applied to the prototype or distance based clustering. This approach was motivated by the Divisive Information-Theoretic Feature Clustering model in probabilistic space with Kullbac…
t-NEB clusters high-dimensional data hierarchically with density paths.
problem Hierarchical clustering struggles with high-dimensional data.
method t-NEB uses density estimation, maximum density paths, and probabilistic merging.
result t-NEB yields state-of-the-art clustering performance on high-dimensional data.
We describe a probabilistic (generative) view of affinity matrices along with inference algorithms for a subclass of problems associated with data clustering. This probabilistic view is helpful in understanding different models and algorithms that are based on affinity functions OF the data. IN particular, we show how(…
New k-means method handles random data better than traditional techniques.
problem Limitations of traditional clustering methods in random data.
method Probabilistic metric space with random normed k-means (RNKM).
result RNKM outperforms traditional methods in complex clustering scenarios.
A probabilistic framework optimizes quantum clustering parameters.
problem Optimizing length parameters for quantum clustering sensitivity.
method Bayesian optimization of control parameters within a probabilistic framework.
result Optimized clustering yields better concordance with known data structure.
Efficiently infers cluster assignments in probabilistic models.
problem Efficiently inferring cluster assignments in probabilistic models.
method Amortized approximate Bayesian inference mapping cluster representations into conditional probabilities.
result Parallelizable, yields iid samples with similar computational cost to Gibbs sampling.
INCAD clusters and detects anomalies in streaming data without thresholds.
problem Clustering and anomaly detection for streaming data with unknown clusters and thresholds.
method Probabilistic clustering and anomaly detection in a streaming model.
result More reliable definition of normal vs abnormal behavior in streaming data.
Improved community detection in graphs with probabilistic models.
problem Lack of probabilistic formulation and fixed number of communities in GNN-based methods.
method Combines GNNs with amortized clustering for variable numbers of clusters.
result Improved performance on synthetic and real datasets compared to previous methods.
clusterBMA combines clustering results from multiple models using Bayesian model averaging.
problem Uncertainty in model selection for clustering.
method Bayesian model averaging to combine results from multiple clustering algorithms.
result ClusterBMA offers probabilistic cluster allocations and quantifies model-based uncertainty.
We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points. The proposed method utilizes the information given by adjacent time points to find the underlying cluster structure and obtain a smooth cluster evolution. This approach allows…
Develops a non-parametric Dirichlet process method for probabilistic biclustering.
problem Challenges in finding biclusters with strong co-occurrence in rows and columns.
method Dual Dirichlet process mixture models for row and column clustering, with cluster number determined by data.
result Improves bicluster extraction in text mining and gene expression analysis.
In this paper, we propose a model-based clustering method (TVClust) that robustly incorporates noisy side information as soft-constraints and aims to seek a consensus between side information and the observed data. Our method is based on a nonparametric Bayesian hierarchical model that combines the probabilistic model …
Develops a method for probabilistic simulation of renewable energy production at grid scale.
problem Uncertainty in short-term electricity generation from renewable assets.
method Probabilistic framework with asset calibration, hierarchical clustering, and Gaussianization.
result Full uncertainty quantification at asset and collection levels.
Proposes PKM for soft K-means clustering.
problem Soft K-means (m=1) unsolved since 1981.
method Probabilistic K-Means (PKM) via nonlinear programming.
result Proposed methods solve PKM efficiently.
Probabilistic embeddings improve speaker diarization accuracy.
problem Improving speaker diarization accuracy using embeddings.
method Extracting x-vectors and precision matrices from speech segments, interfacing with PLDA model, applying agglomerative clustering, joint training of PLDA and extractor.
result Joint training of PLDA and probabilistic x-vector extractor yields accuracy gains.
Entropy regularization improves interpretability of probabilistic clustering models.
problem Bayesian nonparametric mixture models often produce unbalanced cluster frequencies.
method Interpreting the posterior as penalized likelihood, entropy regularization reduces sparsely-populated clusters.
result The proposed entropy-regularized estimator enhances interpretability without sacrificing computational convenience.
A new clustering method using transportation distance for multilevel data.
problem Multilevel clustering problems, especially with large datasets.
method Probabilistic approach based on composite transportation distance, solving a joint optimization problem over probability measures.
result Efficient and scalable solution for multilevel datasets, demonstrated on synthetic and real data.
Bayesian hierarchical clustering (BHC) is an agglomerative clustering method, where a probabilistic model is defined and its marginal likelihoods are evaluated to decide which clusters to merge. While BHC provides a few advantages over traditional distance-based agglomerative clustering algorithms, successive evaluatio…
Notwithstanding the popularity of conventional clustering algorithms such as K-means and probabilistic clustering, their clustering results are sensitive to the presence of outliers in the data. Even a few outliers can compromise the ability of these algorithms to identify meaningful hidden structures rendering their o…
Cross-domain recommendation has been proposed to transfer user behavior pattern by pooling together the rating data from multiple domains to alleviate the sparsity problem appearing in single rating domains. However, previous models only assume that multiple domains share a latent common rating pattern based on the use…
Bayesian method clusters vectorial and network data together.
problem Clustering vectorial and network data simultaneously.
method General probabilistic model with Bayesian inference via MCMC.
result New method outperforms traditional alternatives.
APLC-XLNet improves XMTC by clustering labels and reducing computational time.
problem Efficiently tagging texts with many labels from a large set.
method Fine-tunes XLNet with APLC to approximate cross entropy loss.
result Achieved state-of-the-art results on XMTC benchmarks.
Develops a robust clustering method for uncertain data.
problem Uncertainty in point processes affects clustering accuracy.
method Probabilistic framework based on random labeled point processes.
result Derives an optimal robust clusterer minimizing misclassification.
This paper extends ABCD to discover time series structure using probabilistic program synthesis.
problem Discovering structure in time series data.
method Formulating ABCD in probabilistic program synthesis, using abstract syntax trees and probabilistic programs.
result Improved accuracy in time series clustering and interpolation/extrapolation.
The paper analyzes how extra data affects the accuracy of Bayesian clustering models.
problem The impact of additional data on the accuracy of Bayesian clustering models.
method Theoretical analysis of a complex probabilistic model that includes both initial and additional data.
result The complexity of the model increases, but the additional data can improve accuracy under certain conditions.
Study compares clustering methods for mixed-type data.
problem Challenges in clustering mixed-type data.
method Distance-based (k-prototypes, PDQ, convex k-means), probabilistic (KAY-means, MBNs, LCM).
result KAMILA, LCM, and k-prototypes perform best.
This paper examines variable selection for clustering using Gaussian mixture models.
problem Modern databases require efficient variable selection for clustering models.
method Recalls basics of clustering, examines variable selection methods for model-based clustering.
result Opportunities for improving variable selection methods are presented.
Improved clustering of high-dimensional data subspaces with delayed association.
problem Discovering and clustering subspaces in high-dimensional data.
method Joint optimization approach with delayed association for uncertain points.
result Delayed association improves clustering accuracy, especially for intersecting subspaces.
The paper presents a probabilistic method to discover daily human mobility patterns from mobile data.
problem Discovering daily human mobility patterns from mobile data.
method A non-parameter Bayesian modeling method, Infinite Gaussian Mixture Model, combined with Kullback-Leibler divergence for automatic clustering.
result The IGMM-based algorithm outperforms the GMM-based algorithm in discovering mobility patterns.
Total variation minimization clusters partially labeled data points.
problem Clustering partially labeled data points in stochastic block models.
method Total variation minimization as a clustering method.
result Total variation minimization allows for accurate clustering under certain model parameters.
This study improves author disambiguation without supervision using feature overlap.
problem Author name homonymy in the Web of Science.
method Probabilistic similarity measure based on feature overlap for agglomerative clustering.
result Our approach outperforms the trivial baseline and is state-of-the-art.
HeMPPCAT improves PCA for data with varying noise.
problem PCA's suboptimal performance on data with heterogeneous noise.
method HeMPPCAT uses a GEM algorithm to estimate factors, means, and noise variances.
result Improved factor estimates and clustering accuracy compared to MPPCA.
This paper presents a novel algorithm, based upon the dependent Dirichlet process mixture model (DDPMM), for clustering batch-sequential data containing an unknown number of evolving clusters. The algorithm is derived via a low-variance asymptotic analysis of the Gibbs sampling algorithm for the DDPMM, and provides a h…
Motivation: Public and private repositories of experimental data are growing to sizes that require dedicated methods for finding relevant data. To improve on the state of the art of keyword searches from annotations, methods for content-based retrieval have been proposed. In the context of gene expression experiments, …
New insights into spurious local minima in k-means clustering.
problem Understanding and mitigating spurious local minima in k-means clustering.
method Investigating spurious local minima under a probabilistic generative model.
result Proven structures of spurious local minima for k-means clustering.
Proposes a new model for unsupervised clustering with latent variables.
problem The challenge of unsupervised clustering in machine learning.
method Clustered Generator Model with continuous and discrete latent variables.
result Achieves competitive unsupervised clustering accuracy and disentangled latent representations.
Improved clustering speed for 20 clusters on CIFAR-100 dataset.
problem Training time complexity for VAEs with discrete latent variables is linear in the number of clusters.
method Applied a continuous relaxation to discrete variables in Gaussian Mixture VAE, reducing training time complexity to constant.
result Reduced training time from 47 hours to 6 hours for 20 clusters on CIFAR-100.
Framework reduces data dimensions while preserving structure.
problem Data dimensionality reduction with preserved structure.
method Probabilistic framework integrating generative model and locality info. Structure learning and projection matrix learning coupled.
result Framework learns a smooth skeleton of embedding points forming a graph structure.
PCKID kernel improves spectral clustering on incomplete data.
problem Handling incomplete data in spectral clustering.
method Combining posterior distributions of Gaussian Mixture Models on different scales.
result PCKID kernel outperforms baseline methods for all fractions of missing values.
Unified HDP and LDA models for efficient topic clustering of online course queries.
problem Efficiently cluster and answer subject-specific online course queries.
method Use Hierarchical Dirichlet Process (HDP) to optimize topic number for Latent Dirichlet Allocation (LDA) model runs.
result Achieve optimal clustering efficiency by recursively applying LDA on effective topics.
End-to-end neural network clusters data in one pass.
problem Clustering high-dimensional data like images and speech.
method Trains a neural network to directly output cluster assignments based on perceptual similarity.
result Demonstrates promising performance on COIL-100 and TIMIT datasets.
Paper introduces CGPMs for probabilistic data analysis.
problem Difficulty in applying, combining, and comparing different probabilistic techniques.
method Composable generative population models (CGPMs) that extend graphical models and can describe and compose various probabilistic data analysis techniques.
result CGPMs enable efficient and accurate probabilistic data analysis tasks.
We consider the problem of clustering a set of high-dimensional data points into sets of low-dimensional linear subspaces. The number of subspaces, their dimensions, and their orientations are unknown. We propose a simple and low-complexity clustering algorithm based on thresholding the correlations between the data po…