IEEE

Discriminative Nonnegative Spectral Clustering with Out-of-Sample Extension

Abstract: Data clustering is one of the fundamental research problems in data mining and machine learning. Most of the existing clustering methods, for example, normalized cut and (k)-means, have been ...
IEEE

Bootstrap Deep Spectral Clustering With Optimal Transport

Abstract: Spectral clustering is a leading clustering method. Two of its major shortcomings are the disjoint optimization process and the limited representation capacity. To address these issues, we ...
GitHub

Behrouz-Babaki/COP-Kmeans

usage: run_ckm.py [-h] [--ofile OFILE] [--n_rep N_REP] [--m_iter M_ITER] [--tol TOL] dfile cfile k Run COP-Kmeans algorithm positional arguments: dfile data file cfile constraint file k number of ...
Visual Studio Magazine

Clustering Mixed Categorical and Numeric Data Using k-Means with C#

Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on a "very tricky" machine learning technique. Data clustering is the process of grouping data items together so ...
Scientific Research Publishing

A Study of Triangle Inequality Violations in Social Network Clustering ()

1 Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh. 2 Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany. A social network refers to ...
Frontiers

Spectral density-based clustering algorithms for complex networks

Clustering is usually the first exploratory analysis step in empirical data. When the data set comprises graphs, the most common approaches focus on clustering its vertices. In this work, we are ...
C&EN

Machine Learning Assisted Clustering of Nanoparticle Structures

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. We propose a scheme for the automatic separation (i.e., clustering) of data sets ...
Frontiers

Two-step clustering-based pipeline for big dynamic functional network connectivity data

Dynamic functional network connectivity (dFNC) estimated from resting-state functional magnetic imaging (rs-fMRI) studies the temporally varying functional integration between brain networks. In a ...
GitHub

NMFk: Nonnegative Matrix Factorization + k-means clustering and physics constraints

NMFk is a module of the SmartTensors ML framework (smarttensors.com). NMFk is a novel unsupervised machine learning methodology that allows for the automatic identification of the optimal number of ...
PNAS

Topological clustering of multilayer networks

Multilayer network clustering is used in such diverse areas as optimal islanding of critical infrastructures, analysis of trade agreements, and monitoring ecological interaction patterns. We propose a ...