Descriptor
Cluster Analysis | 5 |
Cluster Grouping | 5 |
Validity | 5 |
Classification | 3 |
Comparative Analysis | 2 |
Matrices | 2 |
Reliability | 2 |
Botany | 1 |
Communication (Thought… | 1 |
Criteria | 1 |
Data Analysis | 1 |
More ▼ |
Author
Breckenridge, James N. | 1 |
Frary, Jewel M. | 1 |
Koch, Valerie L. | 1 |
Krippendorff, Klaus | 1 |
McQuitty, Louis L. | 1 |
Mcquitty, Louis L. | 1 |
Rudnitsky, Alan N. | 1 |
Publication Type
Reports - Research | 2 |
Journal Articles | 1 |
Speeches/Meeting Papers | 1 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating

Mcquitty, Louis L.; Frary, Jewel M. – Educational and Psychological Measurement, 1971
Discussion of a method of classification which attempts to use the particular set of indices of association which produce the most reliable and valid solution. (PR)
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Criteria

McQuitty, Louis L.; Koch, Valerie L. – Educational and Psychological Measurement, 1976
A relatively reliable and valid hierarchy of clusters of objects is plotted from the highest column entries, exclusively, of a matrix of interassociations between the objects. Having developed out of a loose definition of types, the method isolates both loose and highly definitive types, and all those in between. (Author/RC)
Descriptors: Cluster Analysis, Cluster Grouping, Comparative Analysis, Data Analysis

Krippendorff, Klaus – 1977
Clustering techniques seek to group together objects or variables that share some observed qualities or, alternatively, to partition a set of objects or variables into mutually exclusive classes whose boundaries reflect differences in the observed qualities of their members. This paper reviews the general principles underlying clustering…
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Communication (Thought Transfer)

Breckenridge, James N. – Multivariate Behavioral Research, 1989
A Monte Carlo study evaluated the effectiveness of three rules of classifying objects into clusters: nearest neighbor classification; nearest centroid assignment; and quadratic discriminant analysis. Results suggest that the nearest neighbor rule is a useful tool for assessing the validity of the clustering procedure of J. H. Ward (1963). (SLD)
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Discriminant Analysis

Rudnitsky, Alan N. – 1977
Three approaches to the graphic representation of similarity and dissimilarity matrices are compared and contrasted. Specifically, Kruskal's multidimensional scaling, Johnson's hierarchical clustering, and Waern's graphing techniques are employed to depict, in two dimensions, data representing the structure of a set of botanical concepts. Each of…
Descriptors: Botany, Cluster Analysis, Cluster Grouping, Comparative Analysis