• Clustering algorithms🔍
• Comparing clusterings and numbers of clusters by aggregation of ...🔍
• A comparison framework and guideline of clustering methods for ...🔍
• Comparing two clusterings using matchings between ...🔍
• Introducing and Comparing Recent Clustering Methods for Massive ...🔍
• Exploring and Comparing Clusterings of Multivariate Data Sets ...🔍
• Comparing K|means and OPTICS clustering algorithms for ...🔍
• A Comparative Study of Clustering Algorithms🔍

Clustering algorithms: A comparative approach | PLOS ONE

We performed a systematic comparison of 9 well-known clustering methods available in the R language assuming normally distributed data.

Comparing clusterings and numbers of clusters by aggregation of ...

Comparing clusterings and numbers of clusters by aggregation of calibrated clustering validity indexes ... comparative study of cluster validity indices.

A comparison framework and guideline of clustering methods for ...

We compared three classes of performance measures, “precision” as external evaluation, “coherence” as internal evaluation, and stability, of nine methods.

Comparing two clusterings using matchings between ... - Hal-Inria

Comparing two clusterings using matchings between clusters of clusters · Fonction : Auteur · PersonId : 1189617 · ORCID : 0000-0003-2735-6755 · IdRef : ...

Introducing and Comparing Recent Clustering Methods for Massive ...

The silhouette method [52] allows estimating the consistency of the points belonging to clusters. The silhouette value measures how similar an object is to its ...

Exploring and Comparing Clusterings of Multivariate Data Sets ...

Clustering algorithms play an important role in exploratory data analysis. Their partitions help users detect interesting patterns in their data ...

Comparing K-means and OPTICS clustering algorithms for ...

Abstract. The K-means algorithm is the most commonly used clustering method for phonetic vowel description but has some properties that may be ...

A Comparative Study of Clustering Algorithms | by ishika chatterjee

The algorithms under discuss are: k-means algorithm, hierarchical clustering algorithm, self organizing maps algorithm, and expectation maximization clustering ...

Comparing Clusters - NI - National Instruments

Clusters you compare must include the same number of elements, each element in the clusters must be of compatible types, and the elements must be in the ...

Comparing hard and overlapping clusterings - ResearchOnline@JCU

Abstract. Similarity measures for comparing clusterings is an important component, e.g., of evaluating clustering algorithms, for consensus ...

Spatially-Aware Comparison and Consensus for Clusterings

1 Clusters as Distributions. The core idea in doing spatially aware comparison of partitions is to treat a cluster as a distribution over the data, for example ...

Clustering: Metrics for models' comparison and for different ...

Learning more and more about different applications of clustering and the different methods available (K-Means, Gaussian Mixtures, DBSCAN, Hierarchical ...)

Exploring and Comparing Clusterings of Multivariate Data Sets ...

... cluster shapes. In addition, they cannot evaluate individual clusters locally. We present a new measure for assessing and comparing different clusterings ...

lmweber/cytometry-clustering-comparison - GitHub

Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry (CyTOF) data. This repository contains R scripts to reproduce the ...

What is the best way to 'measure' the difference between the result ...

There are many interesting papers on comparing & benchmarking clustering algorithms, mentioning a few methods below.

Exploring and Comparing Unsupervised Clustering Algorithms

The goal of using Gaussian mixture models for clustering applications is similar to the k-means goal of maximizing similarity across observations in a shared ...

A Comparison of Document Clustering Techniques

Hierarchical clustering is often portrayed as the better quality clustering approach, but is limited because of its quadratic time complexity. In contrast, K- ...

A Method for Comparing Two Hierarchical Clusterings - jstor

comparing the results of clustering algorithms are few, and developing inferential methods has proved difficult. Fowlkes and Mallows's contribution is most ...

Comparing clusterings using combination of the kappa statist

The paper focuses on a problem of comparing clusterings with the same number of clusters obtained as a result of using different clustering algorithms.

Use variation of clustering information to compare pairs of splits

Compare a pair of splits viewed as clusterings of taxa, using the variation of clustering information proposed by (Meila 2007) .