Syncytial clustering merges groups from standard algorithms to reveal complex data structures.
problem Challenges in finding clusters with irregular structures.
method Estimates nonparametric overlap between clusters and merges groups with high overlap.
result Always a top performer in identifying groups with regular and irregular structures.
DeCom predicts post-COVID RSV timing and intensity with NPI consideration.
problem Predicting RSV timing and intensity post-COVID with NPI impact.
method Deep coupled tensor factorization machine (DeCom) leveraging tensor factorization and residual modeling.
result DeCom achieves up to 46% lower RMSE and 49% lower MAE compared to baselines.
Study of 13,456 hot stellar systems reveals multi-layered grouping.
problem Understanding physical and evolutionary properties of Hot Stellar Systems.
method Used stellar mass, effective radius, and mass-to-luminosity ratio to group HSS into eight homogeneous ellipsoidal groups, then merged them through a multi-phased syncytial algorithm.
result Identified two complex-structured groups of HSS, one older and smaller, the other brighter and younger.
Convex clustering can only learn convex clusters, with significant gaps between clusters.
problem Understanding the limitations and capabilities of convex clustering.
method Analyzing convex clustering solutions, proving properties, and characterizing clusters.
result Convex clustering can only learn convex clusters with significant gaps between clusters.
Proposes a method to predict cluster number and cluster representatives using cluster stability analysis.
problem Determining the number of clusters in a dataset.
method Analyzes cluster stability using Monte-Carlo simulation to predict cluster number and find cluster representatives.
result Significant improvement in predicting cluster numbers and cluster composition in large datasets.
Paper compares clusterability measures for data mining.
problem Selecting appropriate clusterability measures for data.
method Extensive comparison of clusterability measures.
result Guidelines for selecting suitable measures for clustering.
Proposes a new clustering method based on expectiles for non-spherical clusters.
problem Inability of K-means to handle non-spherical clusters. method Uses expectiles to define cluster centers and searches for clusters via a greedy algorithm.
result Outperforms K-means and spectral clustering on asymmetric shaped clusters. CCMM efficiently solves large-scale convex clustering problems.
problem Scalability and hierarchical structure in convex clustering.
method Majorization-minimization algorithm with cluster fusions and efficient updating.
result CCMM achieves efficient solutions for large datasets.
This paper reviews weighted clustering ensemble methods.
problem Improving clustering results from individual methods.
method Different types of weights and approaches to determining weight values.
result Unified framework for selecting appropriate weighting mechanisms.
Sparse Convex Clustering improves clustering performance in high-dimensional data.
problem Distortion in convex clustering performance with uninformative features.
method Introduces Sparse Convex Clustering with an adaptive group-lasso penalty and a tuning criterion based on clustering stability.
result Demonstrates improved clustering performance through feature selection.
Discussing issues in robust clustering, especially with Gaussian models.
problem Handling outliers and ambiguity in clustering groups.
method Focus on Gaussian mixture model, examining formal definitions, interactions, and tuning decisions.
result Outliers can confuse clustering groups and existing stability measures fail with them.
New indices for determining cluster compactness and separability.
problem Challenges in identifying true clusters in data sets.
method Developed absolute cluster indices to measure compactness and separability.
result Demonstrated improved performance compared to existing indices.
Unified clustering comparison framework for overlapping and hierarchical structures.
problem Critical biases in existing clustering comparison measures.
method Element-centric framework comparing relationships induced by cluster structure.
result Framework does not suffer from biases and provides unique insights.
Mode clustering is a nonparametric method for clustering that defines clusters using the basins of attraction of a density estimator's modes. We provide several enhancements to mode clustering: (i) a soft variant of cluster assignment, (ii) a measure of connectivity between clusters, (iii) a technique for choosing the …
In this paper, a similarity-driven cluster merging method is proposed for unsuper-vised fuzzy clustering. The cluster merging method is used to resolve the problem of cluster validation. Starting with an overspecified number of clusters in the data, pairs of similar clusters are merged based on the proposed similarity-…
This paper introduces a persistence metric to compare clustering solutions with different numbers of clusters.
problem Determining the true number of clusters in a dataset when prior knowledge is lacking.
method The paper introduces a persistence metric based on the maximum over two-norms of all cluster-covariance matrices.
result The persistence metric accurately identifies clustering solutions with the true number of clusters.
New random models improve clustering similarity assessment.
problem Improper random models affect clustering similarity assessments.
method Derived corrected Rand index and Mutual Information measures for varying cluster sizes.
result Random model choice drastically impacts clustering similarity rankings.
In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being ro…
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.
New method finds robust clusters with statistical guarantees.
problem Clustering solutions are unstable and lack robustness guarantees.
method Quantifies cluster instability and finds robust clusters (core clusters).
result Core clusters are more stable and robust to changes in data.
Cluster LOCO: A model-agnostic feature importance score for interpreting cluster outputs
problem Interpreting and auditing cluster outputs
method Cluster LOCO (Leave-One-Covariate-Out)
result More reliably recovers informative features than existing methods
Proposes a deep density-based image clustering method.
problem Challenges in clustering images with unknown cluster number and shape.
method Two-stage approach: feature extraction with CAE and t-SNE, followed by density-based clustering.
result Achieves clustering performance comparable to state-of-the-art methods.
A new distributed clustering framework using distributional kernel.
problem Clustering in distributed networks with arbitrary shapes, sizes, and densities.
method Distributed Clustering based on Distributional Kernel (KDC) using similarity of distributions.
result KDC guarantees equivalent clustering outcomes to centralized methods, reduces runtime, and discovers arbitrary clusters.
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.
A fair clustering method for multiple sensitive attributes is proposed.
problem Ensuring fair representation of sensitive attributes in clustering.
method FairKM (Fair K-Means) method inspired by K-Means, using fairness and coherence objectives.
result FairKM clusters significantly better on both quality and fair representation of sensitive attribute groups.
EAP clusters evolving data, promoting temporal smoothness and automatic cluster tracking.
problem Clustering time-evolving data with temporal smoothness and automatic cluster identification.
method Evolutionary Affinity Propagation (EAP) on a factor graph exchanging messages between adjacent data snapshots.
result EAP clusters data with temporal smoothness and automatically tracks clusters, outperforming existing methods.
OSil algorithm optimizes clustering quality using ASW.
problem Optimizing clustering quality using ASW.
method Distance-based objective function optimizing ASW for clustering.
result OSil algorithm outperforms other clustering methods in clustering quality.
A novel approach ODAR detects outliers for clustering.
problem Outliers interfere with clustering algorithms, leading to unreliable results.
method Feature transformation to separate outliers and normal objects into distinct clusters.
result ODAR improves clustering accuracy on 7 out of 10 datasets.
Exact cluster recovery with same-cluster queries for arbitrary ellipsoidal clusters.
problem Recovering clusters from same-cluster queries in arbitrary ellipsoidal clusters.
method Relaxing spherical k-means assumption to arbitrary ellipsoidal clusters, designing an algorithm with logarithmic query complexity. result Exact recovery of clusters using O(k3lnklnn) queries and ildeO(kn+k3) time. A new method combines spectral and density-based clustering for robust nonconvex clustering.
problem Finding robust clusterings for nonconvex shapes with varying densities and noise.
method Combining spectral and density-based clustering approaches to optimize a density criterion.
result Our method provides robust and reliable clusterings on synthetic and real-world data.
Paper proposes a new co-clustering method for overlapping clusters and outliers.
problem Real-world datasets often contain overlaps and outliers in co-clusters.
method Formulated Non-Exhaustive, Overlapping Co-Clustering problem and developed NEO-CC algorithm.
result NEO-CC algorithm effectively captures underlying co-clustering structure of real-world data.
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.
Network Lasso clusters sparse graph clusters efficiently.
problem Local graph clustering of sparse and chain-like clusters.
method Network Lasso minimizes total variation of cluster indicator signals.
result Network Lasso handles sparse clusters difficult for spectral clustering.
DMClusts discovers multiple clusterings from multi-view data.
problem Finding multiple meaningful and diverse clusterings from multi-view data.
method Deep matrix factorization to gradually factorize multi-view data into representational subspaces and generate one clustering per layer, enforcing diversity through proximity minimization.
result DMClusts outperforms state-of-the-art multiple clustering solutions.
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. 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.
The paper connects two clustering methods by showing gradient ascent flow can move up the cluster tree.
problem Establishing a strong correspondence between clustering methods.
method Moving up the cluster tree by following the gradient ascent flow.
result Gradient ascent flow can be used to move up the cluster tree.
A new clustering method using deep autoencoder networks and spectral clustering.
problem Improving clustering accuracy in noisy data.
method Dual autoencoder network for robust latent representations, mutual information estimation for discriminative features, deep spectral clustering.
result Significantly outperforms state-of-the-art clustering approaches on benchmark datasets.
A new clustering method reduces time and memory usage for massive datasets.
problem Prohibitive computational cost and memory usage of clustering algorithms for massive datasets.
method Iterative hybridized threshold clustering (IHTC) that reduces data points into prototypes and applies clustering algorithms on them.
result IHTC reduces run time and memory usage of k-means and HAC while preserving their performance. 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.
New algorithm clusters hyperspectral images at multiple scales.
problem Clustering hyperspectral images at various scales.
method M-SRDL algorithm using spectral-spatial diffusion distances.
result More accurate clustering labels achieved with spatial regularization.
Graph clustering uses multiscale community detection for improved performance.
problem Improving data clustering accuracy and robustness.
method Graph-theoretical approach combining multiscale community detection.
result Multiscale graph-based clustering achieves better performance than traditional methods.
Parameter-free clustering method using cluster catch digraphs (CCDs).
problem Finding the correct number of clusters in data without specifying a parameter.
method Hybrid of density-based and graph-based clustering methods using Ripley's K function.
result Minimum dominating sets of RK-CCDs estimate and distinguish clusters from noise.
FBC clusters data fairly without needing cluster count.
problem Fairness in clustering groups of different sensitive groups.
method Developed a Bayesian model-based clustering method with a fair prior and efficient MCMC algorithm.
result Reasonably infers the number of clusters and achieves a fair utility trade-off.
A new method for deep clustering uses autoencoded embeddings and local manifold learning.
problem Improving clustering performance in deep learning models.
method Learning an autoencoded embedding, then clustering the underlying manifold using a shallow algorithm.
result UMAP is best at finding the most clusterable manifold in the embedding.
New SDP algorithm recovers large clusters in SBM with small clusters of any size.
problem Graph clustering in SBM with large and small clusters.
method Semidefinite programming (SDP) with novel techniques to handle small clusters.
result Proves exact recovery of large clusters regardless of small cluster sizes.
New visual quality index for fuzzy clustering.
problem No accurate quality index for fuzzy clustering across datasets.
method Proposes a new visual quality index and graph-based solution.
result Validated through extensive experiments on various datasets.
Clustering groups similar data points into clusters.
problem Grouping similar data points into coherent clusters.
method Different clustering methods based on similarity and data representations.
result Various clustering methods exist.