Clustering Algorithms Resources

Published: 27 Aug 2015 Category: machine_learning


Yinyang K-Means: A Drop-In Replacement of the Classic K-Means with Consistent Speedup

Semi-supervised K-means++

k-Means Clustering Is Matrix Factorization

An efficient K-means algorithm for Massive Data

Boost K-Means

Compressive K-means

Convergence rate of stochastic k-means

Fast and Provably Good Seedings for k-Means using k-MC^2 and AFK-MC^2

An efficient K -means clustering algorithm for massive data

Stream Clustering

Neural Network-based Clustering

Spectral Clustering

On Spectral Clustering: Analysis and an algorithm

Hierarchical Clustering

Online Clustering


On Clustering Validation Techniques (2001)

Stream Clustering

Neural network-based clustering using pairwise constraints

PAC-Bayesian Online Clustering

Compressive Spectral Clustering

Interactive Bayesian Hierarchical Clustering

Practical Introduction to Clustering Data

Rényi divergence minimization based co-regularized multiview clustering

Consistent Algorithms for Clustering Time Series

Hybridization of Expectation-Maximization and K-Means Algorithms for Better Clustering Performance

mst_clustering: Clustering via Euclidean Minimum Spanning Trees

k2-means for fast and accurate large scale clustering

Context Aware Nonnegative Matrix Factorization Clustering

Clustering by fast search and find of density peaks

Comment on “Clustering by fast search and find of density peaks”


Clustering datasets


**Introduction to Clustering and Unsupervised Learning PACKT Books**


Finding the K in K-means by Parametric Bootstrap

Random walk vectors for clustering

A comparison between PCA and hierarchical clustering

Visualization of Centroid Movements for K-Means Clustering

K-Means Clustering on Handwritten Digits

Improved Seeding For Clustering With K-Means++ (★★★★★)

Spectral Clustering – How Math is Redefining Decision Making

Visual comparison of machine learning algorithms: Clustering

Clustering Algorithms: From Start To State Of The Art

Hierarchical clustering, using it to invest

Spectral Clustering: A quick overview

Why K-Means is not always a good idea

**High Quality, High Performance Clustering with HDBSCAN SciPy 2016**


MusicMappr: Find patterns in your favorite songs and remix them on the fly!

  • intro: MusicMappr finds chunks of songs that are similar, and clusters them accordingly. You can visualize these clusters and play them back at will. This is for music lovers who are curious about the structures inherent to their favorite songs.
  • github:

TfKmeans: A implementation of k-means clustering in TensorFlow

CUDA K-Means Clustering: A CUDA implementation of the k-means clustering algorithm

kmeans_cuda: CUDA implementation of k-means

K-means in TensorFlow