Semi-supervised clustering methods incorporate a limited amount of supervision into the clustering process. Typically, this supervision is provided by the user in the form of pairwise constraints. Existing methods use such constraints in one of the following ways: they adapt their clustering procedure, their similarity…
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48 results for “supervised clustering”
Clustering is ubiquitous in data analysis, including analysis of time series. It is inherently subjective: different users may prefer different clusterings for a particular dataset. Semi-supervised clustering addresses this by allowing the user to provide examples of instances that should (not) be in the same cluster. …
AutoEmbedder clusters unlabeled data using semi-supervised DNN embedding.
problem Clustering unlabeled data efficiently and effectively.
method Semi-supervised DNN embedding system using Siamese network architecture.
result AutoEmbedder outperforms existing DNN-based semi-supervised methods.
S3C2 uses Siamese networks for semi-supervised clustering with pairwise constraints.
problem Semi-supervised clustering with pairwise constraints.
method S3C2 decomposes SSC into two classification tasks: first, using Siamese networks to label unlabeled pairs; second, using the labeled dataset for clustering.
result S3C2 outperforms existing SSC methods on various datasets.
Cluster-aware Semi-supervised Learning: Relational Knowledge Distillation Provably Learns Clusteringstat.ML
RKD improves clustering in semi-supervised learning with limited labels.
problem Improving clustering accuracy in semi-supervised learning with few labeled examples.
method RKD as spectral clustering on a teacher model's graph, with clustering error quantification.
result RKD provably leads to low clustering error in semi-supervised classification problems.
Paper introduces a method for supervised hierarchical clustering with Exponential Linkage.
problem Discrepancy between training and clustering objectives in supervised clustering.
method Tightly couples supervised training of dissimilarity function with hierarchical clustering, using Exponential Linkage.
result Joint training procedure consistently matches or outperforms other methods, improving dendrogram purity by up to 8 points.
Proposes a semi-supervised K-Means algorithm for better feature selection.
problem Data clustering with unknown feature quality and limited labelled data.
method Combines unsupervised sparse clustering and semi-supervised learning with labelled data.
result The algorithm identifies informative features and maintains high performance.
A new SS-SOM method combines unlabeled and labeled data for clustering and classification.
problem Combining unlabeled and labeled data for clustering and classification in high-dimensional datasets.
method Semi-Supervised Self-Organizing Map (SS-SOM) that dynamically switches between supervised and unsupervised learning.
result SS-SOM outperforms other methods in low-labeled sample conditions and achieves good results with all labeled samples.
A method clusters genes in large gene regulatory networks using semi-supervised hierarchical clustering.
problem Challenging task of identifying interaction clusters in large gene regulatory networks due to data noise and inconsistency.
method SHC-DC: a semi-supervised hierarchical clustering method using deconvolved correlation matrix.
result SHC-DC discovers interaction modules enriched in various signal pathways, validating sleep's impact on interleukin levels and related pathways.
Bayesian Supervised Causal Clustering identifies patient subgroups for personalized decision-making.
problem Finding patient subgroups with similar characteristics for personalized decision-making.
method Bayesian Supervised Causal Clustering (BSCC) that identifies homogenous subgroups based on treatment effects.
result BSCC identifies subgroups with similar covariate profiles and treatment effects.
UCSL combines clustering with supervised learning to discover interpretable subtypes.
problem Discovering interpretable subtypes in datasets relevant to supervised tasks.
method UCSL (Unsupervised Clustering driven by Supervised Learning) framework integrating clustering and supervised learning.
result UCSL achieves +1.9 points in balanced accuracy for psychiatric diseases clustering.
Differentiable clustering method using perturbed spanning forests.
problem Efficient clustering in trainable pipelines with noisy data.
method Stochastic perturbations of minimum-weight spanning forests.
result Method performs well even in challenging settings.
Proposes a graph learning framework for clustering and semi-supervised classification.
problem Graph-based clustering and semi-supervised classification techniques have shown impressive performance but lack explicit cluster structure.
method Uses self-expressiveness of samples for global structure and adaptive neighbor approach for local structure. Incorporates rank constraint to ensure optimal performance.
result The proposed method achieves better performance than state-of-the-art methods in clustering and semi-supervised classification.
Clustering ensemble is one of the most recent advances in unsupervised learning. It aims to combine the clustering results obtained using different algorithms or from different runs of the same clustering algorithm for the same data set, this is accomplished using on a consensus function, the efficiency and accuracy of…
New insights into how data transformations affect self-supervised clustering.
problem Impact of data transformations on self-supervised clustering convergence.
method Theoretical and empirical analysis of various data transformations.
result Certain transformations help in faster convergence of self-supervised clustering.
Pairwise "same-cluster" queries are one of the most widely used forms of supervision in semi-supervised clustering. However, it is impractical to ask human oracles to answer every query correctly. In this paper, we study the influence of allowing "not-sure" answers from a weak oracle and propose an effective algorithm …
SESSC clusters fuzzy rules for TSK classifiers, improving performance with label info.
problem Lack of supervised clustering for TSK fuzzy classifiers.
method SESSC integrates within-cluster compactness, between-cluster separation, and label information.
result SESSC initialization outperforms other clustering methods, especially for small rule numbers.
A new framework for predictive clustering and optimization.
problem Finding clusters of data that yield low error on a supervised target.
method Generalized optimization framework using MILP and MM for scalability.
result Models can uncover different interpretable discrete cluster structures.
Semi-supervised clustering aims to introduce prior knowledge in the decision process of a clustering algorithm. In this paper, we propose a novel semi-supervised clustering algorithm based on the information-maximization principle. The proposed method is an extension of a previous unsupervised information-maximization …
Proposes a method to improve hierarchical clustering using set-level structural priors.
problem Lack of supervision for non-leaf structure in hierarchical clustering.
method Introduces set-level structural priors for semi-supervised hyperbolic hierarchical clustering.
result Improves label consistency and similarity-based tree quality over baselines.
FedMCC learns from distributed data to cluster and extract features.
problem Learning from distributed data and clustering.
method Federated Momentum Contrastive Clustering (FedMCC) framework.
result FedMCC outperforms existing methods in linear evaluation and semi-supervised learning.
In this paper, we focus on finding clusters in partially categorized data sets. We propose a semi-supervised version of Gaussian mixture model, called C3L, which retrieves natural subgroups of given categories. In contrast to other semi-supervised models, C3L is parametrized by user-defined leakage level, which control…
Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship be…
In this paper, we propose a semi-supervised clustering method, CEC-IB, that models data with a set of Gaussian distributions and that retrieves clusters based on a partial labeling provided by the user (partition-level side information). By combining the ideas from cross-entropy clustering (CEC) with those from the inf…
CycleCluster uses clustering to improve deep semi-supervised learning.
problem Difficulty in obtaining labelled data for deep learning.
method Proposes a new framework using clustering regularisation and graph-based pseudo-labels.
result Demonstrates improved predictive capability through numerical results.
Unified framework improves deep multi-view clustering by addressing self-supervision and contrastive alignment issues.
problem Variations in self-supervision-based methods for deep multi-view clustering.
method Unified DeepMVC framework that includes recent methods and leverages self-supervision and contrastive alignment.
result Contrastive alignment negatively impacts cluster separability, especially with many views.
Supervised Convex Clusteringstat.ME
SCC clusters data with supervising variables for better interpretation.
problem Challenging interpretations in unsupervised clustering.
method Joint convex fusion penalty using supervising and unlabeled data.
result Discover new genes and subtypes of Alzheimer's Disease.
Similarity-based clustering and semi-supervised learning methods separate the data into clusters or classes according to the pairwise similarity between the data, and the pairwise similarity is crucial for their performance. In this paper, we propose a novel discriminative similarity learning framework which learns dis…
Contrastive regularization improves semi-supervised learning by better propagating confident pseudo-labels.
problem Consistency regularization's limitation in high performance and efficiency.
method Proposes contrastive regularization to update model features, pushing confident labels into unlabeled samples.
result Improves semi-supervised learning tasks with fewer training iterations and robust performance.
Meta clustering categorizes learners for collaborative learning.
problem Filtering out unqualified collaborators in collaborative learning.
method Select-Exchange-Cluster (SEC) method to classify learners by their supervised functions.
result SEC can cluster learners into accurate collaboration sets and enhance single-learner performance.
A new model SEQ clusters and classifies encoded features for better interpretability.
problem Lack of interpretability in classical supervised classification tasks.
method Proposes a novel supervised learning model named Supervised-Encoding Quantizer (SEQ) that applies a quantizer to cluster and classify encoded features.
result The quantizer provides an interpretable graph where each cluster represents a class with a particular style.
Simplified non-contrastive learning avoids representation collapse.
problem Training failure modes in self-supervised learning.
method Hyperdimensional computing and inductive bias.
result The approach avoids representation collapses.
In this article, the logic rule ensembles approach to supervised learning is applied to the unsupervised or semi-supervised clustering. Logic rules which were obtained by combining simple conjunctive rules are used to partition the input space and an ensemble of these rules is used to define a similarity matrix. Simila…
DGC clusters data with side-information for better prediction.
problem Improving clustering strategies through better prediction performance.
method Deep Goal-Oriented Clustering (DGC) framework that clusters data using supervision and unsupervised modeling.
result Achieves prediction accuracies comparable to state-of-the-art, while learning congruent clustering strategies.
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.
Minimum Density Hyperplanesstat.ML
Associating distinct groups of objects (clusters) with contiguous regions of high probability density (high-density clusters), is central to many statistical and machine learning approaches to the classification of unlabelled data. We propose a novel hyperplane classifier for clustering and semi-supervised classificati…
New methods identify local clusters in graphs with few labels.
problem Identifying specific substructures in large graphs without additional structural information.
method Random sampling, diffusion, and overlap analysis of local clusters.
result Proves the correctness of the proposed methods and achieves state-of-the-art results.
VC-PCR improves prediction by clustering correlated variables.
problem Decreased prediction accuracy due to cluster structure in predictor variables.
method Supervised variable selection and clustering to integrate cluster information into a sparse modeling process.
result VC-PCR achieves better prediction, variable selection, and clustering performance.
Consider a classification problem where we do not have access to labels for individual training examples, but only have average labels over subpopulations. We give practical examples of this setup and show how such a classification task can usefully be analyzed as a weakly supervised clustering problem. We propose thre…
Nowadays, data are generated massively and rapidly from scientific fields as bioinformatics, neuroscience and astronomy to business and engineering fields. Cluster analysis, as one of the major data analysis tools, is therefore more significant than ever. We propose in this work an effective Semi-supervised Divisive Cl…
CURE extracts relations without supervision by clustering similar entity pairs.
problem Extracting relations unsupervised without considering sentence correlations.
method CURE uses Encoder-Decoder architecture for self-supervised learning and clustering similar relations.
result CURE outperforms state-of-the-art models on NYT and UNPC datasets.
A new SOM method learns from both labeled and unlabeled data.
problem Improving performance in semi-supervised learning.
method Adaptive Local Thresholds Semi-Supervised Self-Organizing Map (ALTSS-SOM).
result ALTSS-SOM outperforms other methods in classification and clustering.
We introduce a semi-supervised discrete choice model to calibrate discrete choice models when relatively few requests have both choice sets and stated preferences but the majority only have the choice sets. Two classic semi-supervised learning algorithms, the expectation maximization algorithm and the cluster-and-label…
Local Clustering improves semi-supervised learning models.
problem Improving semi-supervised learning models with limited labeled data.
method Local Clustering (LC) method to mitigate confirmation bias in Mean Teacher (MT) model.
result Adding LC loss to MT improves model performance on semi-supervised benchmark datasets.
Supporting supervised learning in fungal Biosynthetic Gene Cluster discovery: new benchmark datasetsq-bio.GN
New datasets support supervised learning for fungal BGC discovery.
problem Lack of labeled data for fungal BGCs.
method Developed new publicly available datasets for supervised learning.
result Supervised learning outperforms data-driven methods in fungal BGC prediction.
Proposes ConstraintMatch for semi-supervised clustering with unconstrained data.
problem Leveraging unconstrained data alongside constraints for clustering models.
method Semi-supervised context with pseudo-constraining and pseudo-labeling mechanisms.
result Demonstrates effectiveness of ConstraintMatch over baselines.
This paper introduces PM and PMLP to enhance SSL by considering probability density and cluster assumptions.
problem Insufficient utilization of unlabeled data in SSL.
method Introduces PM to discern similarity and PMLP to consider cluster assumption in label propagation.
result PMLP outperforms other methods in SSL tasks.
DIGRAC clusters directed graphs using flow imbalance, outperforming existing methods.
problem Clustering directed networks without label supervision.
method DIGRAC uses a graph neural network with a novel imbalance loss for directed flow imbalance.
result DIGRAC outperforms 10 state-of-the-art methods on directed graph clustering.