Cluster Analysis of High-Dimensional Data: A Case Study

2005 ◽  
pp. 302-310
Author(s):  
Richard Bean ◽  
Geoff McLachlan
Keyword(s):  
Cluster Analysis ◽  
High Dimensional Data ◽  
High Dimensional

scholarly journals How to visualize high-dimensional data: a roadmap

2020 ◽  
Vol Special issue on... ◽  
Author(s):  
Hermann Moisl
Keyword(s):  
Cluster Analysis ◽  
High Dimensional Data ◽  
Latent Structure ◽  
High Dimensional ◽  
Graphical Methods ◽  
Data Set ◽  
The Third ◽  
Historical Text ◽  
International Audience ◽  
And Cluster Analysis

International audience Discovery of the chronological or geographical distribution of collections of historical text can be more reliable when based on multivariate rather than on univariate data because multivariate data provide a more complete description. Where the data are high-dimensional, however, their complexity can defy analysis using traditional philological methods. The first step in dealing with such data is to visualize it using graphical methods in order to identify any latent structure. If found, such structure facilitates formulation of hypotheses which can be tested using a range of mathematical and statistical methods. Where, however, the dimensionality is greater than 3, direct graphical investigation is impossible. The present discussion presents a roadmap of how this obstacle can be overcome, and is in three main parts: the first part presents some fundamental data concepts, the second describes an example corpus and a high-dimensional data set derived from it, and the third outlines two approaches to visualization of that data set: dimensionality reduction and cluster analysis.

scholarly journals Consensus clustering for Bayesian mixture models

2020 ◽  
Author(s):  
Stephen Coleman ◽  
Paul D.W. Kirk ◽  
Chris Wallace
Keyword(s):  
Cluster Analysis ◽  
Mixture Models ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Mcmc Methods ◽  
Consensus Clustering ◽  
Number Of Clusters ◽  
Multiple Dataset ◽  
Bayesian Mixture ◽  
Bayesian Mixture Models

AbstractMotivationCluster analysis is an integral part of precision medicine and systems biology, used to define groups of patients or biomolecules. However, problems such as choosing the number of clusters and issues with high dimensional data arise consistently. An ensemble approach, such as consensus clustering, can overcome some of the difficulties associated with high dimensional data, frequently exploring more relevant clustering solutions than individual models. Another tool for cluster analysis, Bayesian mixture modelling, has alternative advantages, including the ability to infer the number of clusters present and extensibility. However, inference of these models is often performed using Markov-chain Monte Carlo (MCMC) methods which can suffer from problems such as poor exploration of the posterior distribution and long runtimes. This makes applying Bayesian mixture models and their extensions to ‘omics data challenging. We apply consensus clustering to Bayesian mixture models to address these problems.ResultsConsensus clustering of Bayesian mixture models successfully finds generating structure in our simulation study and captures multiple modes in the likelihood surface. This approach also offers significant reductions in runtime compared to traditional Bayesian inference when a parallel environment is available. We propose a heuristic to decide upon ensemble size and then apply consensus clustering to Multiple Dataset Integration, an extension of Bayesian mixture models for integrative analyses, on three ‘omics datasets for budding yeast. We find clusters of genes that are co-expressed and have common regulatory proteins which we validate using external knowledge, showing consensus clustering can be applied to any MCMC-based clustering method.

A Fast Clustering Algorithm for Large-scale and High Dimensional Data

2009 ◽  
Vol 35 (7) ◽  
pp. 859-866
Author(s):  
Ming LIU ◽  
Xiao-Long WANG ◽  
Yuan-Chao LIU
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
High Dimensional Data ◽  
High Dimensional
Sign in / Sign up

Export Citation Format

Share Document