Solves clustering contradictions by high-dimensional embedding with wide gaps.
problem Kleinberg's clustering axioms are contradictory.
method Embedding in high-dimensional space with wide gaps between clusters.
result Handles clustering contradictions by design.
Skeleton clustering detects clusters in high-dimensional data without needing prototypes.
problem Detecting clusters in high-dimensional data with irregular shapes.
method Skeleton clustering combines prototype methods, density-based clustering, and hierarchical clustering using surrogate density measures.
result Skeleton clustering reliably detects clusters in multivariate and high-dimensional data.
This study evaluates clustering algorithms on high-dimensional data.
problem Comparing clustering algorithms on high-dimensional datasets.
method Evaluation of K-means, DBSCAN, and Spectral Clustering using PCA, t-SNE, UMAP, and multiple metrics.
result UMAP preprocessing improves clustering quality across all algorithms, with Spectral Clustering excelling.
HD-BWDM improves clustering validation in high-dimensional data.
problem Determining the right number of clusters in high-dimensional data.
method HD-BWDM integrates random projection, PCA, trimmed clustering, and medoid-based distances.
result HD-BWDM remains stable and interpretable under high-dimensional projections and contamination.
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.
In general, the clustering problem is NP-hard, and global optimality cannot be established for non-trivial instances. For high-dimensional data, distance-based methods for clustering or classification face an additional difficulty, the unreliability of distances in very high-dimensional spaces. We propose a distance-ba…
A clustering algorithm uses the left Gram matrix for high dimensional data.
problem Clustering high dimensional data with many features and few objects.
method The algorithm uses the normalized left Gram matrix G = XX'/P to cluster objects based on row means.
result The algorithm provides the most accurate cluster configuration more than twice as often as competitors.
Two new outlyingness scores improve outlier detection in high-dimensional data.
problem Detecting outliers in high-dimensional data with varying cluster shapes and intensities.
method Outlyingness scores (OOS and IOS) based on Cluster Catch Digraphs (CCDs).
result Both OOS and IOS outperform CCD-based methods in identifying global and local outliers, especially IOS.
New method clusters high-dimensional data with anisotropic noise.
problem Clustering high-dimensional anisotropic mixtures with varying noise structures.
method Covariance Projected Spectral Clustering (COPO) method that projects data onto a low-dimensional space and reassigns clusters based on estimated covariances.
result COPO achieves minimax-optimal misclustering rates in Gaussian settings.
GOLFS selects features for clustering by combining global and local information.
problem Feature selection for high-dimensional clustering without labels.
method Combines global and local information via manifold learning and regularized self-representation.
result Improves feature selection and clustering accuracy.
We present a nonparametric method for selecting informative features in high-dimensional clustering problems. We start with a screening step that uses a test for multimodality. Then we apply kernel density estimation and mode clustering to the selected features. The output of the method consists of a list of relevant f…
DIVI clusters noisy high-dimensional data with stable feature gating.
problem Challenging clustering in high-dimensional noisy data.
method Data-informed variational clustering framework combining global feature gating and adaptive structure growth.
result DIVI performs competitively under severe feature noise and remains computationally feasible.
Unified model for reducing dimensions and clustering high-dimensional data.
problem High-dimensional data clustering and dimensionality reduction.
method Hierarchical mixtures of Gaussians (HMoGs) with closed-form likelihood and inference.
result Efficiently models hundreds of latent dimensions, improving clustering performance.
Study EM and GD for clustering with penalties for misspecification and high dimensions.
problem Clustering with misspecification and high-dimensional data.
method Model-based Gaussian Mixture Models, EM algorithm, GD optimization with AD, penalized likelihood.
result GD outperforms EM on high-dimensional data but both have poor cluster interpretation.
Cluster GARCH model improves multivariate GARCH for high-dimensional asset returns.
problem Modeling high-dimensional asset returns with flexible tail dependencies and cluster structures.
method Introduced a novel multivariate GARCH model with flexible convolution-t distributions, tractable likelihood and derivatives for dynamic correlation structure.
result Cluster GARCH model outperforms existing models in daily returns of 100 assets, both in-sample and out-of-sample.
GCAO improves clustering of high-dimensional data by grouping low-density boundary points.
problem Stability and accuracy of clustering in high-dimensional, non-uniform data.
method Group-level optimization with gravitational attraction and optimization.
result GCAO outperforms 11 clustering methods on multiple datasets.
FCPCA fuzzy clusters high-dimensional time series data efficiently.
problem Ambiguous clustering of multivariate time series data with overlapping distributions.
method FCPCA based on common principal component analysis.
result FCPCA outperforms existing methods in fuzzy clustering of multivariate time series.
We propose an algorithm for clustering high dimensional data. If P features for N objects are represented in an N×P matrix X, where N≪P, the method is based on exploiting the cluster-dependent structure of the N×N matrix XXT. Computational burden thus depends primarily on N, …
Develops a hybrid MtFA approach for high-dimensional data clustering.
problem Scalability issues in traditional MtFA estimation methods for high-dimensional data.
method Integrates profile likelihood method into EM framework for efficient parameter estimation.
result Demonstrates superior computational efficiency and clustering accuracy compared to existing methods.
The high dimensionality of hyperspectral images often results in the degradation of clustering performance. Due to the powerful ability of deep feature extraction and non-linear feature representation, the clustering algorithm based on deep learning has become a hot research topic in the field of hyperspectral remote s…
Subspace clustering aims to find groups of similar objects (clusters) that exist in lower dimensional subspaces from a high dimensional dataset. It has a wide range of applications, such as analysing high dimensional sensor data or DNA sequences. However, existing algorithms have limitations in finding clusters in non-…
A new framework for clustering high-dimensional data using vertical shards.
problem Clustering high-dimensional data with the curse of dimensionality.
method Vertical Consensus Inference (VCI) that splits data into vertical shards for posterior inference.
result VCI can approximate inference on random partitions for high-dimensional data.
A feature-weighted mean shift algorithm improves clustering in high-dimensional data.
problem Clustering high-dimensional data with traditional mean shift algorithms.
method Feature-weighted mean shift algorithm.
result The algorithm outperforms conventional mean shift and preserves computational simplicity.
In several application domains, high-dimensional observations are collected and then analysed in search for naturally occurring data clusters which might provide further insights about the nature of the problem. In this paper we describe a new approach for partitioning such high-dimensional data. Our assumption is that…
A framework for forecasting high-dimensional time-series data using clustering.
problem Forecasting high-dimensional time-series data with intra-cluster similarity.
method Three-stage framework: univariate time series parameter estimation, clustering, multivariate time series parameter computation.
result Framework achieves state-of-the-art results on benchmark datasets, sometimes outperforming deep-learning-based approaches.
Paper proposes efficient methods for clustering and signal recovery in high-dimensional data with block structures.
problem High-dimensional clustering and signal recovery under block signal structures.
method CFA-PCA and MA-PCA methods for sparse and dense block signals.
result Proposed methods achieve computational minimax optimality for clustering and signal recovery.
We present a novel algorithm, called Links, designed to perform online clustering on unit vectors in a high-dimensional Euclidean space. The algorithm is appropriate when it is necessary to cluster data efficiently as it streams in, and is to be contrasted with traditional batch clustering algorithms that have access t…
High dimensional data often contain multiple facets, and several clustering patterns can co-exist under different variable subspaces, also known as the views. While multi-view clustering algorithms were proposed, the uncertainty quantification remains difficult --- a particular challenge is in the high complexity of es…
A new method reduces high-dimensional data's impact on CWMs using TSNE.
problem High-dimensional data hampers CWMs' accuracy and speed.
method TSNE for dimensionality reduction, parsimonious technique, expectation maximization.
result TSNE enhances CWMs' performance in high-dimensional space.
This paper studies clustering and embedding in high-dimensional Gaussian mixture block models.
problem Clustering and embedding in high-dimensional Gaussian mixture block models.
method Spectral clustering and embedding algorithms for graphs sampled from Gaussian mixture block models.
result Performance analysis of spectral clustering and embedding algorithms for 2-component spherical Gaussian mixtures.
CHAODA detects anomalies in high-dimensional data.
problem Anomaly detection in high-dimensional spaces.
method Hierarchical clustering, manifold mapping, transfer learning.
result CHAODA outperforms other algorithms on 16 out of 18 datasets.
Develops a tensor mixture model for high-dimensional data clustering.
problem Jointly modeling and clustering tensors in high dimensions.
method High-dimensional tensor mixture model with plausible dimension reduction assumptions. EHCMA algorithm for efficient estimation.
result The HECM algorithm converges geometrically to a neighborhood within statistical precision of the true parameter.
Paper shows affine constraint is unnecessary for high-dimensional data.
problem The necessity of an affine constraint in affine subspace clustering.
method Theoretical and empirical analysis of conditions for correctness of affine subspace clustering methods.
result Affine constraint has negligible effect on clustering performance for high-dimensional data.
In this paper we consider the problem of clustering collections of very short texts using subspace clustering. This problem arises in many applications such as product categorisation, fraud detection, and sentiment analysis. The main challenge lies in the fact that the vectorial representation of short texts is both hi…
Paper proposes a new clustering model that preserves cluster recovery with fewer dimensions.
problem Clustering high-dimensional data with limited embedding dimensions.
method Randomly projected convex clustering model with improved embedding dimension.
result Cluster recovery can be preserved with fewer dimensions, independent of data points.
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 tackles deep clustering evaluation challenges in high-dimensional data.
problem Evaluation of deep clustering methods is problematic due to the curse of dimensionality and variations in embedding spaces.
method Develops a theoretical framework to highlight the ineffectiveness of internal validation measures and proposes a systematic approach to applying clustering validity indices in deep learning.
result The proposed framework reduces misguidance from improper use of clustering validity indices in deep learning.
In many real life problems, objects are described by large number of binary features. For instance, documents are characterized by presence or absence of certain keywords; cancer patients are characterized by presence or absence of certain mutations etc. In such cases, grouping together similar objects/profiles based o…
This paper studies the optimality of kernel methods in high-dimensional data clustering. Recent works have studied the large sample performance of kernel clustering in the high-dimensional regime, where Euclidean distance becomes less informative. However, it is unknown whether popular methods, such as kernel k-means, …
Efficient clustering for large datasets using a sampling-based approach.
problem Clustering high-dimensional data with a large number of clusters efficiently.
method A simple and efficient clustering method that evaluates distances of data points with a subset of cluster centers.
result Optimal solutions of the approximation are the same as in the exact solution, but more efficient at extracting clusters.
This paper presents a new fuzzy k-means algorithm for the clustering of high-dimensional data in various subspaces. Since high-dimensional data, some features might be irrelevant and relevant but may have different significance in the clustering process. For better clustering, it is crucial to incorporate the contribut…
VillageNet clusters high-dimensional datasets without needing cluster count, using K-Means and WLCF.
problem Clustering large, high-dimensional datasets with diverse variables.
method VillageNet operates in two phases: K-Means for village division and WLCF for network community detection.
result VillageNet autonomously determines optimal number of clusters and outperforms other methods in NMI.
Spatially relaxed inference tackles high-dimensional linear models with correlated covariates.
problem Accurate inference is challenging in high-dimensional settings with spatially correlated covariates.
method Proposes ensembled clustered inference algorithms that control the δ-FWER under standard assumptions. result Ensembled clustered inference algorithms control the δ-FWER and achieve decent power. Proposes a neural network method to improve consistencies in high dimensional data analysis.
problem Inconsistencies among dimensionality reduction, clustering, and visualization tasks in high dimensional data analysis.
method Consistent Representation Learning (CRL) neural network that performs NLDR transformations to satisfy LGP constraints.
result Improves consistencies in data interpretation through end-to-end task execution.
Robust clustering of high-dimensional data is an important topic because clusters in real datasets are often heavy-tailed and/or asymmetric. Traditional approaches to model-based clustering often fail for high dimensional data, e.g., due to the number of free covariance parameters. A parametrization of the component sc…
We consider the problem of clustering with the longest-leg path distance (LLPD) metric, which is informative for elongated and irregularly shaped clusters. We prove finite-sample guarantees on the performance of clustering with respect to this metric when random samples are drawn from multiple intrinsically low-dimensi…
Sparse prototypes improve clustering of high-dimensional directional data.
problem Clustering high-dimensional directional data like texts.
method Estimate a von Mises mixture using l1 penalized likelihood and EM algorithm.
result Sparse prototypes enhance interpretability and clustering performance.