Weighting and selection of variables for cluster analysis

1995 ◽  
Vol 12 (1) ◽  
pp. 113-136 ◽  
Author(s):  
R. Gnanadesikan ◽  
J. R. Kettenring ◽  
S. L. Tsao
Keyword(s):  
Cluster Analysis ◽  
Selection Of Variables ◽  
Selection Of

Selection of Variables in Cluster Analysis: An Empirical Comparison of Eight Procedures

Psychometrika ◽  
2007 ◽  
Vol 73 (1) ◽  
pp. 125-144 ◽  
Author(s):  
Douglas Steinley ◽  
Michael J. Brusco
Keyword(s):  
Cluster Analysis ◽  
Empirical Comparison ◽  
Selection Of Variables ◽  
Selection Of

scholarly journals Selection of Variables for Cluster Analysis and Classification Rules

2008 ◽  
Vol 103 (483) ◽  
pp. 1294-1303 ◽  
Author(s):  
Ricardo Fraiman ◽  
Ana Justel ◽  
Marcela Svarc
Keyword(s):  
Cluster Analysis ◽  
Classification Rules ◽  
Selection Of Variables ◽  
Selection Of

Cluster Analysis of Antigenic Profiles of Tumors: Selection of Number of Clusters Using Akaike’s Information Criterion

1990 ◽  
Vol 29 (03) ◽  
pp. 200-204 ◽  
Author(s):  
J. A. Koziol
Keyword(s):  
Cluster Analysis ◽  
Basic Problem ◽  
Information Criterion ◽  
Akaike's Information Criterion ◽  
Cell Surface Antigens ◽  
Number Of Clusters ◽  
Akaike’S Information Criterion ◽  
Multinomial Data ◽  
Tumor Types ◽  
Selection Of

AbstractA basic problem of cluster analysis is the determination or selection of the number of clusters evinced in any set of data. We address this issue with multinomial data using Akaike’s information criterion and demonstrate its utility in identifying an appropriate number of clusters of tumor types with similar profiles of cell surface antigens.

Using clusterization principles for the selection of repair sections of main oil pipelines based on diagnostic data

2011 ◽  
Vol 8 (1) ◽  
pp. 201-210
Author(s):  
R.M. Bogdanov
Keyword(s):  
Cluster Analysis ◽  
Defect Density ◽  
Density Variation ◽  
Distance Functions ◽  
Oil Pipeline ◽  
Oil Pipelines ◽  
Classification Of Images ◽  
Main Oil Pipeline ◽  
Selection Of

The problem of determining the repair sections of the main oil pipeline is solved, basing on the classification of images using distance functions and the clustering principle, The criteria characterizing the cluster are determined by certain given values, based on a comparison with which the defect is assigned to a given cluster, procedures for the redistribution of defects in cluster zones are provided, and the cluster zones parameters are being changed. Calculations are demonstrating the range of defect density variation depending on pipeline sections and the universal capabilities of linear objects configuration with arbitrary density, provided by cluster analysis.

Using Cluster Analysis to Improve Marketing Experiments

1971 ◽  
Vol 8 (3) ◽  
pp. 340-347 ◽  
Author(s):  
George S. Day ◽  
Roger M. Heeler
Keyword(s):  
Cluster Analysis ◽  
Space Representation ◽  
Reduced Space ◽  
High Degree ◽  
Degree Of Similarity ◽  
Sensitive Experiment ◽  
Selection Of

When the selection of a sample of stores or cities requires a high degree of similarity among the test units in order to ensure a sensitive experiment, the sample may no longer represent the market. These conflicting requirements can be satisfied by choosing the sample from clusters displayed in a reduced space representation of the market.

scholarly journals State of the art in selection of variables and functional forms in multivariable analysis—outstanding issues

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Willi Sauerbrei ◽  
◽  
Aris Perperoglou ◽  
Matthias Schmid ◽  
Michal Abrahamowicz ◽  
...  
Keyword(s):  
State Of The Art ◽  
Multivariable Analysis ◽  
Selection Of Variables ◽  
Functional Forms ◽  
Selection Of
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