Proposes a hierarchical clustering method for positive and negative dissimilarities.
problem Clustering dissimilarities, especially positive and negative.
method Hierarchical correlation clustering followed by tree preserving embedding.
result Performance on various datasets.
We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps, and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finally we descr…
Posterior regularization enhances Bayesian hierarchical mixture clustering by improving node separation.
problem High nodal variance in BHMC trees, leading to weak separation between nodes at higher levels.
method Employing Posterior Regularization to impose max-margin constraints on nodes at every level.
result Improves cluster separation in BHMC models, enhancing overall model performance.
A clustering algorithm for natural hierarchical clusters with near-linear time complexity.
problem Hierarchical clustering with near-linear time complexity.
method Nearest neighbor based clustering algorithm that defines clusters naturally.
result Near-linear time and space complexity for certain datasets.
There has been a surge in the number of large and flat data sets - data sets containing a large number of features and a relatively small number of observations - due to the growing ability to collect and store information in medical research and other fields. Hierarchical clustering is a widely used clustering tool. I…
The paper extends fairness to hierarchical clustering, finding efficient algorithms with minimal loss.
problem Ensuring fairness in hierarchical clustering where data is recursively partitioned.
method Extending fairness to hierarchical clustering, developing simple, efficient algorithms for various objectives.
result Simple, efficient algorithms for fair hierarchical clustering with only a negligible loss in objective.
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.
Bagging and boosting are proved to be the best methods of building multiple classifiers in classification combination problems. In the area of "flat clustering" problems, it is also recognized that multi-clustering methods based on boosting provide clusterings of an improved quality. In this paper, we introduce a novel…
Interactive steering improves hierarchical clustering for diverse user needs.
problem Existing hierarchical clustering methods fail to meet diverse user needs.
method Knowledge-driven and data-driven constraints, interactive steering through a visual interface.
result Facilitates the building of customized clustering trees efficiently and effectively.
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.
Randomized hierarchical clustering tests for stability and detects clusters.
problem Greedy hierarchical clustering's sensitivity to data perturbations.
method Randomization scheme and p-values at each node.
result Valid hypothesis testing procedures for clustering results.
A hierarchical clustering algorithm for data clouds without structure assumptions.
problem Exploring data clouds without making structure assumptions.
method Hierarchical topological clustering algorithm that infers persistence of outliers and clusters of arbitrary shape from data hierarchy.
result The algorithm can provide meaningful clusters in complex datasets.
Defines hierarchical clustering axioms for various densities.
problem Defining hierarchical clustering for different types of densities.
method An axiomatic approach to piecewise constant densities, then extending to general densities.
result Our axiomatic definition results in Hartigan's cluster tree under certain conditions.
Extends clustering method to cost-based hierarchies.
problem Guaranteeing near-optimality in hierarchical clustering.
method Optimization-based Sublevel Set method extended to cost-based hierarchies.
result Ensures clustering is nearly optimal without distributional assumptions.
Tree-SNE combines t-SNE and hierarchical clustering for data visualization.
problem Data visualization and clustering in complex datasets.
method Stacked one-dimensional t-SNE embeddings and alpha-clustering.
result Effective hierarchical clustering and visualization of various datasets.
Paper develops a new objective for hierarchical clustering in Euclidean space.
problem Hierarchical clustering in Euclidean space with dissimilarity scores.
method Develops a new global objective and connects it to bisecting k-means.
result Optimal 2-means solution approximates the new objective, proving bisecting k-means optimizes a natural global objective.
The joint optimization of representation learning and clustering in the embedding space has experienced a breakthrough in recent years. In spite of the advance, clustering with representation learning has been limited to flat-level categories, which often involves cohesive clustering with a focus on instance relations.…
The problem of hierarchical clustering items from pairwise similarities is found across various scientific disciplines, from biology to networking. Often, applications of clustering techniques are limited by the cost of obtaining similarities between pairs of items. While prior work has been developed to reconstruct cl…
Locally adaptive clustering for tree delineation.
problem Tree delineation from distance data.
method Locally adaptive hierarchical cluster termination.
result Multi-scale alternative to conventional termination criteria.
Method preserves order in hierarchical clustering of ordered data.
problem Order preserving hierarchical clustering of directed acyclic graphs.
method Combination of classical hierarchical clustering and ultrametric fitting.
result Optimal clustering preserves both cluster quality and order.
A new hierarchical clustering method selects representative points from sub-minimum-spanning-trees.
problem Selecting representative points for hierarchical clustering to improve robustness and reliability.
method Identify representative points using reciprocal nearest data points in sub-minimum-spanning-trees.
result The proposed algorithm outperforms other methods in accuracy and efficiency.
Paper tackles online hierarchical clustering, offering efficient algorithms with good quality clusters.
problem Offline hierarchical clustering algorithms require full dataset, limiting their use on large datasets.
method Proposes two online algorithms (OTD and OHAC) to optimize Moseley and Wang revenue function.
result OTD achieves 1/3-approximation to MW revenue under data separation assumption.
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…
HiPart offers an efficient, interactive tool for hierarchical clustering.
problem Efficient and interpretable hierarchical clustering for Big Data.
method Divisive hierarchical clustering algorithms with interactive visualizations.
result High computational efficiency and interpretability in Big Data applications.
New clustering method recovers hidden tree structure from data.
problem Recovering hidden hierarchical structure in data.
method Maximum average dot product for merging clusters in hierarchical clustering.
result The algorithm produces a tree that accurately represents the underlying generative hierarchical structure.
Grinch efficiently clusters large datasets with complex structures.
problem Large-scale hierarchical clustering with complex linkage functions.
method Rotate and graft subroutines for efficient reconfiguration.
result Grinch guarantees accurate cluster trees for consistent models.
Hierarchical clustering based on pairwise similarities is a common tool used in a broad range of scientific applications. However, in many problems it may be expensive to obtain or compute similarities between the items to be clustered. This paper investigates the hierarchical clustering of N items based on a small sub…
Improves hierarchical clustering in Euclidean space using autoencoders.
problem Lack of unsupervised methods for learning hierarchical structure in Euclidean space.
method Variational autoencoder with Gaussian mixture prior, rescaling latent space, and Ward's linkage.
result Improved dendrogram purity and Moseley-Wang cost function results.
FISHDBC clusters arbitrary data with flexible, scalable, and hierarchical features.
problem Clustering arbitrary data with arbitrary distance functions efficiently.
method Flexible, incremental, scalable, hierarchical density-based clustering algorithm.
result Flexible clustering of arbitrary data without feature extraction.
The Baire metric induces an ultrametric on a dataset and is of linear computational complexity, contrasted with the standard quadratic time agglomerative hierarchical clustering algorithm. In this work we evaluate empirically this new approach to hierarchical clustering. We compare hierarchical clustering based on the …
New A* algorithm improves hierarchical clustering quality.
problem Improving hierarchical clustering quality in large search spaces.
method Combining A* search with a trellis data structure.
result Achieves higher quality results than baselines in particle physics and other benchmarks.
Clustering partitions a dataset such that observations placed together in a group are similar but different from those in other groups. Hierarchical and K-means clustering are two approaches but have different strengths and weaknesses. For instance, hierarchical clustering identifies groups in a tree-like structure b…
New method uses dendrograms for better mixture model selection and clustering.
problem Selecting the correct number of components in finite mixture models.
method Hierarchical clustering tree derived from overfitted latent mixing measures.
result Consistently selects the true number of mixing components and optimal convergence rate for parameter estimation.
This paper considers networks where relationships between nodes are represented by directed dissimilarities. The goal is to study methods for the determination of hierarchical clusters, i.e., a family of nested partitions indexed by a connectivity parameter, induced by the given dissimilarity structures. Our constructi…
CoHiRF extends clustering methods to handle high-dimensional data efficiently.
problem Scalability limits of existing clustering methods.
method Hierarchical consensus framework operating on label assignments.
result Improves robustness and scalability to high-dimensional noise.
Novel graph network learns hierarchical network structure.
problem Lack of information in hierarchical network topology.
method Hierarchical clustering for multiscale decomposition, graph convolutional layers.
result Competitive performance on citation network benchmark.
Exact hierarchical clustering algorithms for data analysis.
problem Finding meaningful structures in data like phylogenetic trees.
method Novel dynamic-programming algorithms based on a trellis data structure.
result Exact computation of partition function, maximum likelihood hierarchy, and marginal probabilities.
We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing it with the underlying industrial activity structure. Specifically, we apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hi…
MultiDendrograms is a Java-written application that computes agglomerative hierarchical clusterings of data. Starting from a distances (or weights) matrix, MultiDendrograms is able to calculate its dendrograms using the most common agglomerative hierarchical clustering methods. The application implements a variable-gro…
Fair HAC algorithms ensure clustering fairness across protected groups.
problem Ensuring clustering fairness in HAC algorithms when datasets contain biases.
method Proposes fair algorithms for HAC that enforce fairness constraints regardless of distance linkage criteria.
result Our fair HAC algorithms find fairer clusterings compared to vanilla HAC and other fair clustering approaches.
Extends convex clustering to graph-structured data.
problem Handling graph-structured data with convex clustering.
method Formulates a convex objective and uses a proximal dual algorithm for efficient recovery.
result Demonstrates the effectiveness of the method on real-life datasets.
Hierarchical clustering uses OWA operators to generalize linkage methods and avoid dendrogram inversions.
problem Avoiding unaesthetic inversions in hierarchical clustering dendrograms.
method OWA-based linkages combined with the Lance-Williams formula and conditions on weight generators.
result Conditions for weight generators to produce dendrograms without inversions.
We propose an algorithm, HPREF (Hierarchical Partitioning by Repeated Features), that produces a hierarchical partition of a set of clusterings of a fixed dataset, such as sets of clusterings produced by running a clustering algorithm with a range of parameters. This gives geometric structure to such sets of clustering…
This paper introduces hierarchical quasi-clustering methods, a generalization of hierarchical clustering for asymmetric networks where the output structure preserves the asymmetry of the input data. We show that this output structure is equivalent to a finite quasi-ultrametric space and study admissibility with respect…
The Ward error sum of squares hierarchical clustering method has been very widely used since its first description by Ward in a 1963 publication. It has also been generalized in various ways. However there are different interpretations in the literature and there are different implementations of the Ward agglomerative …
VLAC clusters data hierarchically, outperforming GMM.
problem Clustering with multiple attributes or hierarchies.
method Disentangled latent representations for hierarchical clustering.
result VLAC outperforms Gaussian Mixture Models in clustering accuracy.
Stable density-based clustering via multiparameter persistence.
problem Density-based clustering stability to data perturbations.
method Degree-Rips construction, correspondence-interleaving distance, multiparameter stability analysis.
result Persistable pipeline yields stable, consistent density-based clustering.
Proposes HypCSE for enhanced hierarchical clustering.
problem Challenges in existing hierarchical clustering methods.
method Hyperbolic Continuous Structural Entropy (HypCSE) neural networks.
result Superior performance on seven datasets.