Advanced Clustering Algorithms Research

Clustering of the structures by using \"snakes-and-dragons\" approach, or correlation matrix as a signal

The paper introduces a new method for clustering correlation matrices called 'snakes-and-dragons' and applies it to neuroscience, human microbiome, and macroeconomics data.

Deep Augmented Multiview Clustering

This paper introduces a deep learning method for multiview clustering that co-clusters matrices and identifies cross-matrix cluster associations, with initial results on a clinical dataset.

Clusterdv, a simple density-based clustering method that is robust, general and automatic.

Clusterdv is a density-based clustering method that automatically determines cluster number and distribution without manual parameter adjustment, performing well on synthetic and experimental data.

CluSim: a Python package for the comparison of clusterings and dendrograms

CluSim is a Python package that provides over 20 similarity measures for comparing clusterings and dendrograms, with examples on gene expression data and phylogenetic trees.

DocClustering: An implementation of the novel PS-Document Clustering algorithm

DocClustering implements a novel PS-Document Clustering algorithm for biomedical literature, offering improved accuracy over conventional methods.

Clustering trees: a visualisation for evaluating clusterings at multiple resolutions

The paper introduces clustering trees to visualize clustering results at multiple resolutions and demonstrates their use on simulated and real datasets.

The Impact of Random Models on Clustering Similarity

Corrected Rand index and mutual information under random ensembles where cluster number and sizes vary; the chosen null model can reorder which clusterings look most alike.

Shared Nearest Neighbor clustering in a Locality Sensitive Hashing framework

The paper introduces LSH-SNN, a clustering algorithm for high-dimensional sequence data, applied to metagenomics to reconstruct genomes from environmental samples without reference data.

Rigid geometry solves "curse of dimensionality" effects in clustering methods: An application to omics data

The paper applies rigid geometry with a p-adic metric to overcome clustering issues in omics data caused by the curse of dimensionality, showing reproducible results across methods.

Emergent community agglomeration from data set geometry

This paper introduces effective dissimilarity transformation (EDT) to address the curse of dimensionality by transforming data geometry, improving clustering performance.