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…
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Locally Adaptive Hierarchical Cluster Termination With Application To Individual Tree Delineationstat.ML
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.
Standard agglomerative clustering suggests establishing a new reliable linkage at every step. However, in order to provide adaptive, density-consistent and flexible solutions, we study extracting all the reliable linkages at each step, instead of the smallest one. Such a strategy can be applied with all common criteria…
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…
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 …
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…
Versatile linkage: a family of space-conserving strategies for agglomerative hierarchical clusteringstat.ME
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…
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.
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…
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 consider the problem of fast time-series data clustering. Building on previous work modeling the correlation-based Hamiltonian of spin variables we present an updated fast non-expensive Agglomerative Likelihood Clustering algorithm (ALC). The method replaces the optimized genetic algorithm based approach (f-SPC) wit…
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.
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…
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.
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.
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…
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.
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.
Agglomerative hierarchical clustering (AHC) requires only the similarity between objects to be known. This is attractive when clustering signals of varying length, such as speech, which are not readily represented in fixed-dimensional vector space. However, AHC is characterised by space and time complexity, ma…
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 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.
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 paper presents a novel adaptive resonance theory (ART)-based modular architecture for unsupervised learning, namely the distributed dual vigilance fuzzy ART (DDVFA). DDVFA consists of a global ART system whose nodes are local fuzzy ART modules. It is equipped with the distinctive features of distributed higher-ord…
A comparative study of general fuzzy min-max neural networks for pattern classification problemscs.LG
General fuzzy min-max (GFMM) neural network is a generalization of fuzzy neural networks formed by hyperbox fuzzy sets for classification and clustering problems. Two principle algorithms are deployed to train this type of neural network, i.e., incremental learning and agglomerative learning. This paper presents a comp…
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…
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 space and trades off space and time with parameter .
Hierarchical graph clustering is a common technique to reveal the multi-scale structure of complex networks. We propose a novel metric for assessing the quality of a hierarchical clustering. This metric reflects the ability to reconstruct the graph from the dendrogram, which encodes the hierarchy. The optimal represent…
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.
Recursive nearest agglomeration (ReNA): fast clustering for approximation of structured signalsstat.ML
In this work, we revisit fast dimension reduction approaches, as with random projections and random sampling. Our goal is to summarize the data to decrease computational costs and memory footprint of subsequent analysis. Such dimension reduction can be very efficient when the signals of interest have a strong structure…
Symbolic Data Analysis is based on special descriptions of data - symbolic objects (SO). Such descriptions preserve more detailed information about units and their clusters than the usual representations with mean values. A special kind of symbolic object is a representation with frequency or probability distributions …
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.
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. We apply the Baire distance to spectrometric and photometric redshifts from the Sloan Digital Sky Survey using, in this work, about…
Dynamic time warping (DTW) can be used to compute the similarity between two sequences of generally differing length. We propose a modification to DTW that performs individual and independent pairwise alignment of feature trajectories. The modified technique, termed feature trajectory dynamic time warping (FTDTW), is a…
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.
The exponential growth of complex data demands fully automatic clustering. Gaussian mixture models (GMMs) provide uncertainty-aware grouping but often require expertise to specify hyperparameters, e.g., component count and covariance structure. While mclust (R) automates this via Bayesian Information Criterion (BIC), P…
Neural networks using transformer-based architectures have recently demonstrated great power and flexibility in modeling sequences of many types. One of the core components of transformer networks is the attention layer, which allows contextual information to be exchanged among sequence elements. While many of the prev…
Fuzzy Jetshep-ph
Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets. To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative hierarchical clustering schemes known as sequential recombination. W…
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.
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.
Bayesian Rose Treescs.LG
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…
The study uses DCC for financial market analysis, revealing hidden correlations.
problem Identifying hidden nonlinear correlations in financial markets.
method Agglomerative hierarchical clustering with distance correlation coefficient.
result DCC reveals more information than Pearson correlation for financial data.
Efficient Monte Carlo and greedy heuristic for the inference of stochastic block modelsphysics.data-an
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…
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.
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.
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. Detecting hidden variables poses two problems: determining the relations to other variables in the model and determining the number of states of …