Search 250 million research papers

Cluster Analysis

Daniel S. Wilks · International geophysics/International geophysics series · 2011

Cited by 125

Cluster analysis.

Ana Campa (65860), Juan José Ferreira (5884025) · Figshare · 2018

<p>Phylogenetic relationship of the 70 Vaccinium spp accessions based on the analysis of 5255 SNPs obtained by GBS. Red color indicates varieties with low chill requirements under local growing conditions.</p>

Cited by 0Open access

Cluster analysis.

Alexandra Y. Grün (5395001), Constantin B. App (5394998), Andreas Breidenbach (5394995) et al. · Figshare · 2018

<p>Cluster analysis based on the Bray Curtis index for <i>Msp</i>I (A) and <i>BstU</i>I (B) and the corresponding MDS plot for <i>Msp</i>I (C) and <i>BstU</i>I (D).</p>

Cited by 0Open access

Cluster analysis.

Hermann Cuntz (32618), Friedrich Forstner (244431), Juergen Haag (95455) et al. · Figshare · 2015

<p>(A) Dendrite spanning fields are readily separable into the individual cell\n types at the example here of two parameters only: the convexity and the\n relative location to the axonal axis (B) Cluster analysis using three\n parameters of a generalized extreme value distribution fits for branching\n properties from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000251#pcbi-1000251-g001" target="_blank">Figure 1E, 1G,\n and 1K</a>.</p>

Cited by 0Open access

Cluster analysis.

Alexander F. Humphreys (5742746), Jochen Halfar (5742749), James C. Ingle (5742752) et al. · Figshare · 2018

<p>Cluster analysis (Chord Distance; Ward Linkage) on Hellinger-transformed count data for 28 foraminifera species making up top 75% of production. Sample sites from individual clusters are marked by grey symbols for corresponding plots within CCA.</p>

Cited by 0Open access

Cluster analysis.

Giordano Mancini (114178), Ilda D’Annessa (115140), Andrea Coletta (115142) et al. · Figshare · 2015

<p>Overlap of the linker representative clusters in the binary (<b>A1 and A2</b>) and ternary (<b>B1 and B2</b>) complexes. The linker is represented as a coil and the color scale is red-white-blue from the most to the less populated cluster. On the top (A1 and B1) the representative structures obtained upon fitting on the linker itself are reported, while on the bottom (A2 an B2) the representative structures obtained upon fitting on the core and C-terminal domains of the protein.</p>

Cited by 0Open access

Cluster analysis.

Marco Borri (799311), Maria A. Schmidt (799312), Ceri Powell (799313) et al. · Figshare · 2015

<p><b>(a)</b> Cumulative distribution of voxels (primary tumours + lymph nodes), partitioned with k = 4, in the bi-dimensional space formed by the two parameters IAUGC60 and ADC. IAUGC60 is expressed in units of mmol·s, ADC in units of ×10<sup>−3</sup> mm<sup>2</sup>/s. <b>(b,c)</b> Corresponding cluster analysis maps of a responding (b) and a non-responding (c) lymph node, at baseline and post-induction chemotherapy (IC). Different colours indicate a difference in functionality at baseline.</p>

Cited by 0Open access

Cluster analysis

M SONG · OSF Preprints (OSF Preprints) · 2020

Cited by 0

Cluster Analysis

Barry J. Everitt, Sabine Landau, Morven Leese et al. · Wiley series in probability and statistics · 2011

Cited by 2,639

Cluster Analysis

Kata Csizér, Joan Jamieson · The Encyclopedia of Applied Linguistics · 2012

Abstract “Cluster analysis is the art of finding groups in data” (Kaufman & Rousseeuw, 1990, p. 1).

Cited by 12

Cluster analysis.

Maria Virgínia Alves Martins (695129), Frederico Silva (695130), Lazaro L. M. Laut (695131) et al. · Figshare · 2015

<p>a) Based on: grain size data, fine fraction (Fine) and sand and gravel content (Sand), mineralogical data: calcite (calc), opal C/T (Opal) and pyrite (Pyr) content, geochemical data such as total concentrations of Ca and S, total organic carbon (TOC) and carbohydrates (CHO), lipids (LIP), proteins (PTN) and total of biopolymers (TBP) as well as the PLI values (metals enrichment index), total available concentrations (TAC) and C/S ratio values, physicochemical parameters: temperature (Temp), foraminiferal density (FD), Shannon Index (H’). b) Based on: total of biopolymers (TBP), total available concentrations (TAC), potential redox (Eh), salinity (Sal) and pH, pyrite (Pyr) and the percentage of the most frequent species. C) Based on: biopolymers rations (PTN/CHO+LIP, CHO/PTN+LIP, LIP/PTN+CHO) and the percentage of the most frequent species.</p>

Cited by 0Open access

Cluster Analysis

NAKHLA, GHADA · Edge Hill University Research Information Repository (Edge Hill University) · 2025

Cluster analysis is an unsupervised learning method that organises related data points into groups based on shared attributes. This technique identifies underlying patterns in datasets without prior labelling, focusing on maximising internal similarity among items within a cluster while maintaining clear distinctions between different clusters. It is used across various disciplines to uncover hidden structures and inform decision-making processes. The methodology employs algorithms to evaluate similarities and differences, ensuring that elements within a cluster exhibit greater similarity than those in other clusters.

Cited by 0

Cluster analysis.

Silvia Riva (300774), Marco Monti (300775), Paola Iannello (300776) et al. · Figshare · 2015

Cited by 0Open access

Cluster analysis

Everitt, Brian S, Landau, Sabine, Leese, Morven et al. · CERN Document Server (European Organization for Nuclear Research) · 2011

Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics.This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.Real life examples are used throughout to demons

Cited by 0

Cluster Analysis

Richard Huntsinger · Cambridge University Press eBooks · 2025

Business analytics is all about leveraging data analysis and analytical modeling methods to achieve business objectives. This is the book for upper division and graduate business students with interest in data science, for data science students with interest in business, and for everyone with interest in both. A comprehensive collection of over 50 methods and cases is presented in an intuitive style, generously illustrated, and backed up by an approachable level of mathematical rigor appropriate to a range of proficiency levels. A robust set of online resources, including software tools, coding examples, datasets, primers, exercise banks, and more for both students and instructors, makes the book the ideal learning resource for aspiring data-savvy business practitioners.

Cited by 0

Cluster Analysis

Wolfgang Karl Härdle, Léopold Simar, Matthias R. Fengler · 2024

Cited by 0

Cluster Analysis

Wilson, Michael · Figshare · 2020

The dataset derives from responses of 369 Australian nurses to a survey on intentions to respond to requests for voluntary assisted dying

Cited by 0Open access

Cluster analysis

Dennis Tay · 2024

Cited by 0

Cluster analysis

Aldenderfer, Mark 1950-, Blashfield, Roger K. · 1984

This book is designed to be an introduction to cluster analysis for those with no background and for those who need an up-to-date and systematic guide through the maze of concepts, techniques, and algorithms associated with the clustering data. The authors begin by discussing measures of similarity, the input needed to perform any clustering analysis. They note varying theoretical meanings of the concept and discuss the set of empirical measures most commonly used to measure similarity. Various methods for actually identifying the clusters are then described. Finally, they discuss procedures for validating the adequacy of a cluster analysis. At all points, the differing concepts and techniques are compared and evaluated.

Cited by 0

Cluster Analysis

Parteek Bhatia · Cambridge University Press eBooks · 2019

Chapter Objectives✓ To comprehend the concept of clustering, its applications, and features.✓ To understand various distance metrics for clustering of data.✓ To comprehend the process of K-means clustering.✓ To comprehend the process of hierarchical clustering algorithms.✓ To comprehend the process of DBSCAN algorithms.

Cited by 0