Advanced Clustering Algorithms Research

Evaluating Clustering and Imputation Approaches for Multivariate Longitudinal Clinical Data Using a Real-World eCDF-Copula Resampling Framework

The paper evaluates clustering methods for multivariate longitudinal data using a real-world eCDF-Copula resampling framework to address missing data and improve benchmarking.

Benchmarking Clustering Strategies for High-Dimensional Spike Time-Windows Data from Multi-Electrode Arrays

This study benchmarks clustering methods for high-dimensional spike time-window data from MEAs, finding that PCA with k-means and Euclidean distance performs best.

HDCluster: High-Degree Graph Clustering for Robust Analysis of Single Molecule Localization Microscopy

HDCluster is a high-degree graph clustering algorithm for SMLM data that efficiently handles large datasets and outperforms existing methods in speed and noise robustness.

Multiresolution Clustering of Genomic Data

This paper introduces a framework for multi-resolution clustering of genomic data to uncover nested biological relationships and reconcile clustering discrepancies across datasets.

Density-based optimization for unbiased, reproducible clustering applied to single molecule localization microscopy

This paper introduces DBOpt, a method to optimize density-based clustering parameters for SMLM data, improving clustering accuracy and reproducibility.

BitBIRCH Clustering Refinement Strategies

The paper introduces BitBIRCH clustering refinement strategies to improve clustering quality without sacrificing efficiency, with new post-processing tools and examples.

Extended Quality (eQual): Radial threshold clustering based on n-ary similarity

This paper improves Radial Threshold Clustering by reducing its time complexity to O(N) and making clustering results order-invariant.

Clustering individuals using INMTD: a novel versatile multi-view embedding framework integrating omics and imaging data

INMTD integrates omics and 3D imaging data to produce unconfounded individual subgroups with biologically meaningful embeddings.

Physically Interpretable Performance Metrics for Clustering

The paper introduces two physically interpretable clustering metrics for evaluating MD simulation data, showing they align with physical intuition across different systems.

SillyPutty: Improved clustering by optimizing the silhouette width

SillyPutty improves clustering by optimizing silhouette width, showing comparable accuracy to existing methods and better performance when combined with hierarchical clustering.

Exploring automated Feature Selection for Model-based and Density-based clustering with application to NCI 60 data

The paper compares feature selection methods for clustering, finding that principal component-based k-means outperforms Clustvarsel and DBSCAN on NCI-60 data.

A hyperparameter-randomized ensemble approach for robust clustering across diverse datasets

This paper introduces an ensemble clustering method with hyperparameter randomization that improves clustering accuracy and interpretability across diverse datasets.

netMUG: a novel network-guided multi-view clustering workflow for dissecting genetic and facial heterogeneity

netMUG identifies actionable subgroups using network-guided multi-view clustering, showing improved performance over existing methods in synthetic and real data.

KMD clustering: Robust general-purpose clustering of biological data

KMD clustering provides a robust, general-purpose method for biological data by eliminating cryptic hyperparameters and achieving high performance across datasets.

CommonNNClustering--A Python package for generic common-nearest-neighbour clustering

The paper introduces a Python package for common-nearest-neighbour clustering with hierarchical clustering and result evaluation functionalities.

GeoWaVe: Geometric median clustering with weighted voting for ensemble clustering of cytometry data

GeoWaVe improves cytometry data clustering by combining geometric median clustering with weighted voting, enhancing robustness and reproducibility across different algorithms.

ECCO: A Python UI for Performing Ensemble Clustering Combined with Cluster Optimization on Omics Data

ECCO provides a Python UI for ensemble clustering and optimization on omics data, enabling efficient integration of advanced clustering methods.

Fast multi-resolution consensus clustering

This paper introduces RCL, a parameter-free consensus clustering method that integrates multiple flat clusterings into a multi-resolution view, validated on single-cell transcriptome data from human developing kidneys.

A scalable and unbiased discordance metric with H+

The paper introduces H+, a scale-agnostic and group-balance invariant discordance metric for clustering, and provides scalable methods to compute it.

Gradient-k: Improving the performance of K-Means using the density gradient

Gradient-k improves k-means clustering by incorporating density gradients, enhancing accuracy and reducing iterations, with applications in neural spike sorting.

Improve consensus partitioning via a hierarchical procedure

This paper introduces hierarchical consensus partitioning to improve subgroup identification in high-throughput data, demonstrating its effectiveness on a DNA methylation dataset.

Optimization algorithm for omic data subspace clustering

The paper introduces a method for subspace clustering of omic data and shows it outperforms existing techniques in identifying ground-truth subspaces.

Scalable Clustering with Supervised Linkage Methods

This paper introduces a novel hierarchical density clustering algorithm, HAL-x, which efficiently generates multiple cluster labels for single-cell data and achieves high accuracy in classifying clinical statuses.

Partition Quantitative Assessment (PQA): A quantitative methodology to assess the embedded noise in clustered omics and systems biology data

The paper introduces a method to quantify noise in clustered biological data using autocorrelation.

Comparison of silhouette-based reallocation methods for vegetation classification

This paper introduces REMOS, a new reallocation method for vegetation classification, and compares it with OPTSIL, finding REMOS superior in reducing negative silhouette widths and runtime efficiency.

Hypercluster: a flexible tool for parallelized unsupervised clustering optimization

Hypercluster automates and accelerates unsupervised clustering by testing multiple models and hyperparameters in parallel, providing reproducible results.

Scalable hierarchical clustering by composition rank vector encoding and tree structure

This paper introduces a hierarchical clustering algorithm that avoids record comparison, achieving linear time complexity for high-dimensional data with strong nonlinear correlations.

A Hierarchical Clustering Algorithm Based on Silhouette Index for Cancer Subtype Discovery from Omics Data

This paper introduces SilHAC, a hierarchical clustering algorithm that automatically estimates the number of clusters in cancer omics data and outperforms existing methods.

Silhouette width using generalized mean - a flexible method for assessing clustering efficiency

The paper introduces a generalized silhouette width method that adjusts sensitivity to cluster compactness and separation, improving assessment of clustering efficiency in heterogeneous data.

Cluster Tendency Assessment in Neuronal Spike Data

The paper evaluates methods for estimating cluster structure in neuronal spike data and introduces iVAT for visualization without dimensionality reduction, showing t-SNE provides more reliable clustering results.