New method extracts all reliable linkages in agglomerative clustering.
problem Finding optimal clustering solutions with adaptive and flexible criteria.
method Extracting all reliable linkages at each step for various criteria.
result Single linkage criterion yields minimum spanning tree.
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 …
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…
A new clustering method versatile linkage improves on existing strategies.
problem Improving agglomerative hierarchical clustering methods.
method Introducing versatile linkage, a family of clustering strategies based on generalized means.
result Versatile linkage is space-conserving compared to existing methods.
New algorithm clusters time-series data faster and more efficiently.
problem Fast clustering of noisy, correlated time-series data.
method Agglomerative Likelihood Clustering (ALC) replaces genetic algorithm with recursive merging.
result ALC reduces compute time and resource usage for large datasets.
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.
New clustering method for symbolic data combines leaders and agglomerative approaches.
problem Clustering of symbolic data with modal values.
method Proposed a clustering criterion function and leaders clustering method.
result Efficiently solves clustering problems with large data sets.
ANN clusters multi-view data by agglomerating subviews and avoiding postprocessing.
problem Challenges in multi-view clustering, especially subview agglomeration.
method Agglomerative Neural Network (ANN) using Constrained Laplacian Rank.
result ANN outperforms state-of-the-art methods in multi-view clustering.
This work improved clustering methods by analyzing various datasets and dendrograms.
problem Avoiding false positives in clustering, especially for unimodal and bimodal data.
method Applied agglomerative clustering methods (single, average, median, complete, centroid, Ward's) to various datasets.
result Many methods detected two clusters in unimodal data, with single-linkage being more resilient.
This manuscript develops the theory of agglomerative clustering with Bregman divergences. Geometric smoothing techniques are developed to deal with degenerate clusters. To allow for cluster models based on exponential families with overcomplete representations, Bregman divergences are developed for nondifferentiable co…
Large scale agglomerative clustering is hindered by computational burdens. We propose a novel scheme where exact inter-instance distance calculation is replaced by the Hamming distance between Kernelized Locality-Sensitive Hashing (KLSH) hashed values. This results in a method that drastically decreases computation tim…
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…
We introduce a new Bayesian model for hierarchical clustering based on a prior over trees called Kingman's coalescent. We develop novel greedy and sequential Monte Carlo inferences which operate in a bottom-up agglomerative fashion. We show experimentally the superiority of our algorithms over others, and demonstrate o…
Paper proposes a faster method for fuzzy neural networks by removing unsuitable hyperboxes.
problem Reducing training time for fuzzy neural networks.
method A novel hyperbox selection rule based on mathematical formulas to remove unsuitable hyperboxes.
result Significant decrease in training time for both online and agglomerative learning algorithms.
Enhances explainability of AI models without sacrificing accuracy.
problem Lack of interpretability in black-box models like Deep Neural Networks and Gradient Boosting.
method Co-supervised Local Model Synthesis (SynthTree) using Mixture of Linear Models (MLM).
result Statistical models significantly enhance explainability of AI models.
This paper presents algorithms for hierarchical, agglomerative clustering which perform most efficiently in the general-purpose setup that is given in modern standard software. Requirements are: (1) the input data is given by pairwise dissimilarities between data points, but extensions to vector data are also discussed…
We examine methods for clustering in high dimensions. In the first part of the paper, we perform an experimental comparison between three batch clustering algorithms: the Expectation-Maximization (EM) algorithm, a winner take all version of the EM algorithm reminiscent of the K-means algorithm, and model-based hierarch…
New clustering method uses Wasserstein distance to analyze simulation outputs.
problem Analyzing stochastic simulation outputs to uncover relationships and patterns.
method Agglomerative clustering using regularized Wasserstein distance.
result Identifies staffing plans yielding similar performance outcomes.
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.
Paper presents a faster method for computing cost of equity and performing comparable company analysis.
problem Tedium and subjectivity in traditional cost of equity and comparable company analysis methods.
method Uses spectral and agglomerative clustering to compute cost of equity and perform comparable company analysis.
result Reduces time required for comps by orders of magnitude and improves consistency and reliability.
Study compares GFMM neural networks for pattern classification.
problem Pattern classification problems.
method Two algorithms (incremental and agglomerative learning) to train GFMM neural networks.
result GFMM neural networks show strong and weak points on benchmark datasets.
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.
DDVFA learns and retrieves clusters without order dependence, outperforming other methods.
problem Order dependence in clustering methods.
method DDVFA combines distributed higher-order activation and match functions with dual vigilance parameters.
result DDVFA outperforms other clustering methods in online learning mode.
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…
Paper tackles noisy comparison oracle for robust clustering algorithms.
problem Finding robust clustering algorithms under noisy comparison oracle.
method Develops algorithms for k-center clustering and agglomerative hierarchical clustering using noisy comparison oracle.
result Proves robust algorithms achieve good approximation guarantees with high probability.
This paper proposes a simple but effective graph-based agglomerative algorithm, for clustering high-dimensional data. We explore the different roles of two fundamental concepts in graph theory, indegree and outdegree, in the context of clustering. The average indegree reflects the density near a sample, and the average…
ReNA fast clusters features for structured signals, reducing analysis time and noise.
problem Efficiently summarize structured signals to reduce analysis time and noise.
method Recursive Nearest Agglomeration (ReNA) for linear-time feature clustering.
result ReNA approximates data as well as traditional methods but with linear time complexity.
Develops a method to infer partial rankings from sparse comparisons.
problem Challenges in ranking items with limited and noisy comparisons.
method Nonparametric Bayesian approach for learning partial rankings.
result Finds partial rankings that distinguish meaningful differences only when data supports it.
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 …
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.
Improved space management in iterative clustering reduces subset growth without sacrificing performance.
problem Iterative re-clustering of speech segments can lead to unchecked subset growth, compromising performance.
method Integration of a space management strategy into the iterative process of hierarchical clustering.
result No loss in performance in terms of F-measure while guaranteeing space complexity.
New method detects organized eCommerce fraud by clustering orders.
problem Detecting coordinated fraud by groups of fraudsters.
method Scalable categorical clustering using agglomerative clustering and sampling.
result Groups 35-45% of fraudulent orders together, detects 26.2% of fraud with low false alarms.
cuSLINK clusters data faster on GPUs, saving space and time.
problem Efficiently clustering large datasets on GPUs.
method Reformulated SLINK algorithm on GPU with optimized building blocks.
result cuSLINK uses O(Nk) space and trades off space and time with parameter k. Bayesian context trees capture complex dependencies in categorical sequences.
problem Complex, long-range dependencies in categorical sequences are not well captured by simple models.
method Parsimonious Bayesian context trees with model-based agglomerative clustering for efficient inference.
result The proposed framework outperforms existing models on real-world data.
Method selects valid IVs from a large set using clustering and test of overidentifying restrictions.
problem Selecting valid instrumental variables from a large set of candidates.
method Agglomerative hierarchical clustering combined with a test of overidentifying restrictions.
result Achieves oracle properties when the largest group of IVs is valid.
New metric assesses hierarchical clustering quality.
problem Evaluate the quality of hierarchical graph clustering.
method Proposes a novel metric based on dendrogram reconstruction.
result Optimal graph representation leads to regular dendrograms.
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.
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.
Hierarchical structure is ubiquitous in data across many domains. There are many hierarchical clustering methods, frequently used by domain experts, which strive to discover this structure. However, most of these methods limit discoverable hierarchies to those with binary branching structure. This limitation, while com…
We present an efficient algorithm for the inference of stochastic block models in large networks. The algorithm can be used as an optimized Markov chain Monte Carlo (MCMC) method, with a fast mixing time and a much reduced susceptibility to getting trapped in metastable states, or as a greedy agglomerative heuristic, w…
Paper proposes a statistical approach for predicting lane changes in highway scenarios.
problem Early recognition of lane changes for automated driving.
method Prototype trajectories generated from real data using Agglomerative Hierarchical Clustering. Maneuver prediction via Boosted Decision Trees and mixture model.
result Improved performance for lane change and trajectory prediction compared to a reference approach.
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.
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. We apply the Baire distance to spectrometric and photometric redshifts from the Sloan Digital Sky Survey using, in this work, about…
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.
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.
MSTs provide a fast and meaningful clustering method in low-dimensional data.
problem Quantifying the effectiveness of MSTs in low-dimensional clustering tasks.
method Identifying upper bounds for MST performance, reviewing and extending existing MST-based partitioning schemes.
result MST methods can be very competitive, often outperforming traditional clustering algorithms.
We have measured the dissimilarities among several printed characters of a single page in the Gutenberg 42-line bible and we prove statistically the existence of several different matrices from which the metal types where constructed. This is in contrast with the prevailing theory, which states that only one matrix per…
In this paper, we deal with the problem of curves clustering. We propose a nonparametric method which partitions the curves into clusters and discretizes the dimensions of the curve points into intervals. The cross-product of these partitions forms a data-grid which is obtained using a Bayesian model selection approach…