Suchergebnisse - Determining the number of clusters in a data set

Effects of Resampling in Determining the Number of Clusters in a Data Set

Autoren: Friedrich Leisch Rainer Dangl

Quelle: Journal of Classification. 37:558-583

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 0101 mathematics, 01 natural sciences

Zugangs-URL: https://dblp.uni-trier.de/db/journals/classification/classification37.html#DanglL20
https://doi.org/10.1007/s00357-019-09328-2
https://link.springer.com/article/10.1007/s00357-019-09328-2

An in depth evaluation of internal criteria for determining the number of clusters in a data set

Autoren: Stevens, Jordan Steinley, Douglas

Weitere Verfasser: Stevens, Jordan Steinley, Douglas

Dateibeschreibung: 1 online resource (viii, 60 pages) : illustration

A Graph Theoretic Criterion for Determining the Number of Clusters in a Data Set.

Autoren: Krolak-Schwerdt, Sabine Eckes, Thomas

Quelle: Multivariate Behavioral Research. 1992 27(4):541-565.

Peer Reviewed: Y

Page Count: 25

Descriptors: Cluster Analysis, Data Collection, Equations (Mathematics), Evaluation Criteria, Mathematical Models

An Examination of Indexes for Determining the Number of Clusters in Binary Data Sets.

Autoren: Dimitriadou, Evgenia Dolnicar, Sara Weingessel, Andreas

Quelle: Psychometrika. Mar 2002 67(1):137-160.

Peer Reviewed: Y

Page Count: 24

Descriptors: Cluster Analysis, Simulation

A new validation index for determining the number of clusters in a data set

Autoren: Shengrui Wang Qingshan Jiang Haojun Sun

Quelle: IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222). 3:1852-1857

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://ieeexplore.ieee.org/document/938445/

A Graph Theoretic Criterion for Determining the Number of Clusters in a Data Set

Autoren: Sabine Krolak-Schwedt Thomas Eckes

Quelle: Multivariate Behavioral Research. 27:541-565

Schlagwörter: 0504 sociology, 05 social sciences, 0101 mathematics, 01 natural sciences

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/26811133
https://www.ncbi.nlm.nih.gov/pubmed/26811133
http://europepmc.org/abstract/MED/26811133
https://orbilu.uni.lu/handle/10993/8214
https://core.ac.uk/display/17190561

Methods of Determining the Number of Clusters in a Data Set and a New Clustering Criterion

Autoren: Yan, Mingjin

Thesis Advisors: Statistics, Ye, Keying, Prins, Samantha C. Bates, Spitzner, Dan J., Smith, Eric P.

Schlagwörter: Gap statistic, Multi-layer clustering, DD-weighted gap statistic, Cluster analysis, Weighted gap statistic, Number of clusters, K-means clustering

Dateibeschreibung: application/pdf

Relation: Proposal-Face.pdf

Verfügbarkeit: http://hdl.handle.net/10919/29957
http://scholar.lib.vt.edu/theses/available/etd-12062005-153906/

Comparative Assessment of Some Selected Methods of Determining the Number of Clusters in a Data Set

Autoren: Korzeniewski, Jerzy University of Łódź, Department of Statistical Methods

Weitere Verfasser: Korzeniewski, Jerzy University of Łódź, Department of Statistical Methods

Schlagwörter: number of clusters, computer algorithm, mean shift method, cluster analysis

Dateibeschreibung: application/pdf

Zugangs-URL: https://hdl.handle.net/11089/16838

Effects of Resampling in Determining the Number of Clusters in a Data Set.

Autoren: Dangl, Rainer Leisch, Friedrich

Quelle: Journal of Classification. Oct2020, Vol. 37 Issue 3, p558-583. 26p.

Schlagwörter: *Algorithms, Industrial clusters, Model validation

Determining the number of clusters in categorical data

Autoren: Silvestre, Cláudia Cardoso, Margarida Figueiredo, Mário et al.

Weitere Verfasser: Silvestre, Cláudia Cardoso, Margarida Figueiredo, Mário et al.

Schlagwörter: Cluster analysis, Model selection, Categorical variables

Dateibeschreibung: application/msword

Verfügbarkeit: http://hdl.handle.net/10400.21/4048

An examination of procedures for determining the number of clusters in a data set

Autoren: André Hardy

Quelle: Studies in Classification, Data Analysis, and Knowledge Organization ISBN: 9783540584254

Zugangs-URL: https://researchportal.unamur.be/en/publications/db2c0ed9-30e6-4470-9b48-41e5c665567a
https://rd.springer.com/chapter/10.1007/978-3-642-51175-2_20
https://link.springer.com/chapter/10.1007%2F978-3-642-51175-2_20

Determining the Number of Clusters using Neural Network and Max Stable Set Problem.

Autoren: Karim, Awatif Loqman, Chakir Boumhidi, Jaouad

Quelle: Procedia Computer Science. 2018, Vol. 127, p16-25. 10p.

Schlagwörter: Artificial neural networks, Artificial intelligence, Cluster analysis (Statistics), Data science, K-means clustering, Text mining, Data mining

Determining the Number of Clusters in a Data Set Without Graphical Interpretation

Autoren: Aguirre, Nathan S Davies, Misty D

Schlagwörter: Computer Systems

Zugangs-URL: https://ntrs.nasa.gov/citations/20110016534

An Examination of Procedures for Determining the Number of Clusters in a Data Set

Autoren: Martha C. Cooper Glenn W. Milligan

Quelle: Psychometrika. 50:159-179

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://econpapers.repec.org/RePEc:spr:psycho:v:50:y:1985:i:2:p:159-179
https://psycnet.apa.org/record/1985-27071-001
https://rd.springer.com/article/10.1007/bf02294245
https://ci.nii.ac.jp/naid/30017109040
https://link.springer.com/article/10.1007%2FBF02294245
https://ideas.repec.org/a/spr/psycho/v50y1985i2p159-179.html

An examination of procedures for determining the number of clusters in a data set, New approaches in Classification and Data Analysis, 178-185

Autoren: Hardy, André

Zugangs-URL: https://researchportal.unamur.be/en/publications/9cc54ec6-dcfe-4c0e-99a2-7596d481fe30

Methods of Determining the Number of Clusters in a Data Set and a New Clustering Criterion

Autoren: Yan, Mingjin Statistics Ye, Keying et al.

Weitere Verfasser: Yan, Mingjin Statistics Ye, Keying et al.

Schlagwörter: Cluster analysis, DD-weighted gap statistic, K-means clustering, Weighted gap statistic, Gap statistic, Multi-layer clustering, Number of clusters

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10919/29957
http://scholar.lib.vt.edu/theses/available/etd-12062005-153906/

Determination of the Number of Clusters in a Data Set: A Stopping Rule × Clustering Algorithm Comparison

Autoren: Derrick S. Boone

Quelle: Management Theories and Strategic Practices for Decision Making ISBN: 9781466624733

Schlagwörter: 4. Education, 0101 mathematics, 01 natural sciences, 3. Good health

Zugangs-URL: https://www.igi-global.com/article/determination-number-clusters-data-set/60528
https://doi.org/10.4018/jsds.2011100101
http://dblp.uni-trier.de/db/journals/ijsds/ijsds2.html#Boone11
https://dblp.uni-trier.de/db/journals/ijsds/ijsds2.html#Boone11

PERFORMANCE EVALUATION OF SELECTED DISTANCE-BASED AND DISTRIBUTION-BASED CLUSTERING ALGORITHMS

Autoren: Adeleke Raheem Ajiboye

Quelle: International Journal of Software Engineering and Computer Systems. 4:38-48

Schlagwörter: Artificial intelligence, Data stream clustering, Cluster (spacecraft), 02 engineering and technology, Pattern recognition (psychology), Clustering Algorithms, Cluster analysis, Artificial Intelligence, Document Clustering, Shape Matching and Object Recognition, 0202 electrical engineering, electronic engineering, information engineering, Image Compression Techniques and Standards, CURE data clustering algorithm, Canopy clustering algorithm, Data mining, Data Clustering Techniques and Algorithms, Single-linkage clustering, Correlation clustering, Semi-supervised Clustering, Computer science, Programming language, Algorithm, Computer Science, Physical Sciences, Computer Vision and Pattern Recognition, Determining the number of clusters in a data set, Stream Data Clustering, Density-based Clustering

Zugangs-URL: https://journal.ump.edu.my/ijsecs/article/download/194/24
https://journal.ump.edu.my/ijsecs/article/view/194

An Heterogeneous Population-Based Genetic Algorithm for Data Clustering

Autoren: Cherif Bouras Mohamed Tahar Kimour Amina Bedboudi

Quelle: Indonesian Journal of Electrical Engineering and Informatics (IJEEI). 5

Schlagwörter: FOS: Computer and information sciences, Artificial intelligence, Population, 02 engineering and technology, Clustering Algorithms, Cluster analysis, Sociology, Artificial Intelligence, Data Mining Techniques and Applications, Document Clustering, Machine learning, 0502 economics and business, 0202 electrical engineering, electronic engineering, information engineering, Swarm Intelligence Optimization Algorithms, CURE data clustering algorithm, Canopy clustering algorithm, Adaptation to Concept Drift in Data Streams, Data mining, Demography, Data Clustering Techniques and Algorithms, 05 social sciences, Correlation clustering, Semi-supervised Clustering, Computer science, FOS: Sociology, Algorithm, Genetic algorithm, Computer Science, Physical Sciences, Crossover, Determining the number of clusters in a data set, Stream Data Clustering, Density-based Clustering, Information Systems

Zugangs-URL: http://section.iaesonline.com/index.php/IJEEI/article/download/299/200
http://section.iaesonline.com/index.php/IJEEI/article/download/299/200
http://section.iaesonline.com/index.php/IJEEI/article/view/299

An examination of procedures for determining the number of clusters in a data set.

Autoren: Milligan, Glenn Cooper, Martha

Quelle: Psychometrika; Jun1985, Vol. 50 Issue 2, p159-179, 21p