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| Educational and Psychological… | 6 |
| Multivariate Behavioral… | 6 |
| Journal of the American… | 4 |
| Psychometrika | 3 |
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| Journal Articles | 14 |
| Reports - Research | 10 |
| Reports - Evaluative | 6 |
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Peer reviewedVan Mechelen, Iven; And Others – Psychometrika, 1995
This paper describes the conjunctive counterpart of the hierarchical classes model of P. De Boeck and S. Rosenberg. The new model represents the row-column association as a conjunctive function of a set of hypothetical binary variables. The substantive significance of the conjunctive model is illustrated. (Author/SLD)
Descriptors: Cluster Analysis, Matrices
Peer reviewedMardberg, Bertil – Educational and Psychological Measurement, 1975
Descriptors: Cluster Analysis, Computer Programs, Matrices
Peer reviewedMcQuitty, Louis L. – Educational and Psychological Measurement, 1983
Iterative Intercolumnar Correlation Classification (IICC) computes the correlation coefficients for the entries of every column of a matrix with those of every other column of the matrix. Iteration increases the size and validity of the object indices, reduces error in the indices, and increases homogeneity amongst them. (Author/BW)
Descriptors: Classification, Cluster Analysis, Correlation, Error Patterns
Peer reviewedTzeng, Oliver C. S.; May, William H. – Educational and Psychological Measurement, 1979
A strategy for reordering the hierarchical tree structure is presented. While the order of terminal nodes of Johnson's procedure is arbitrary, this procedure will rearrange every triad of nodes under a common least upper node so that the middle node is nonarbitrarily closest to the anchored node. (Author/CTM)
Descriptors: Cluster Analysis, Cluster Grouping, Matrices, Multidimensional Scaling
Peer reviewedPeay, Edmund R. – Psychometrika, 1975
Peay presented a class of grouping methods based on the concept of the r-clique for symmetric data relationships. The concepts of the r-clique can be generalized readily to directed (or asymmetric) relationships, and groupings based on this generalization may be found conveniently using an adoption of Peay's methodology. (Author/BJG)
Descriptors: Classification, Cluster Analysis, Cluster Grouping, Mathematical Models
Peer reviewedBurton, Michael L. – Multivariate Behavioral Research, 1975
Three dissimilarity measures for the unconstrained sorting task are investigated. All three are metrics, but differ in the kind of compensation which they make for differences in the sizes of cells within sortings. Empirical tests of the measures are done with sorting data for occupations names and the names of behaviors, using multidimensional…
Descriptors: Classification, Cluster Analysis, Correlation, Matrices
Peer reviewedMather, Laura A. – Journal of the American Society for Information Science, 2000
Discussion of models for information retrieval focuses on an application of linear algebra to text clustering, namely, a metric for measuring cluster quality based on the theory that cluster quality is proportional to the number of terms that are disjoint across the clusters. Explains term-document matrices and clustering algorithms. (Author/LRW)
Descriptors: Algorithms, Cluster Analysis, Information Retrieval, Mathematical Formulas
Peer reviewedMcQuitty, Louis L.; Koch, Valerie L. – Educational and Psychological Measurement, 1975
A rapid method for hierarchically clustering the n objects of a matrix which portrays the interrelation of every object to every other object, where n equals any number up to 1,000 and even larger, is developed and discussed. Results compare favorably with those from other excellent methods. (Author/BJG)
Descriptors: Cluster Analysis, Comparative Analysis, Evaluation Methods, Matrices
Peer reviewedLevine, Marilyn M.; Levine, Leonard P. – Information Processing and Management, 1984
Presents system for automatic handling of ordered sets, states based on these sets, and differing points of view regarding Universe of Discourse. Aspects are represented by new logical "overlap" function with examples taken from Ranganathan's horse and carriage parable and several books involving four main concepts (history, geography,…
Descriptors: Cluster Analysis, Cluster Grouping, Diagrams, Information Retrieval
Peer reviewedSchaffer, Catherine M.; Green, Paul E. – Multivariate Behavioral Research, 1996
The common marketing research practice of standardizing the columns of a persons-by-variables data matrix prior to clustering the entities corresponding to the rows was evaluated with 10 large-scale data sets. Results indicate that the column standardization practice may be problematic for some kinds of data that marketing researchers used for…
Descriptors: Cluster Analysis, Comparative Analysis, Marketing, Matrices
Peer reviewedRodgers, Joseph Lee; Thompson, Tony D. – Applied Psychological Measurement, 1992
A flexible data analysis approach is proposed that combines the psychometric procedures seriation and multidimensional scaling. The method, which is particularly appropriate for analysis of proximities containing temporal information, is illustrated using a matrix of cocitations in publications by 18 presidents of the Psychometric Society.…
Descriptors: Citations (References), Cluster Analysis, Mathematical Models, Matrices
Peer reviewedCattell, Raymond B.; Burdsal, Charles A. – Multivariate Behavioral Research, 1975
Descriptors: Cluster Analysis, Factor Analysis, Factor Structure, Item Analysis
Hubert, Lawrence; Schultz, James – 1975
An empirical assesssment of the space distortion properties of two prototypic hierarchical clustering procedures is given in terms of an occupancy model developed from combinatorics. Using one simple example, the single-link and complete-link clustering strategies now in common use in the behavioral sciences are empirically shown to be space…
Descriptors: Behavioral Sciences, Classification, Cluster Analysis, Cluster Grouping
Peer reviewedKatz, Jeffrey Owen; Rohlf, F. James – Multivariate Behavioral Research, 1975
Descriptors: Cluster Analysis, Comparative Analysis, Correlation, Factor Analysis
Peer reviewedMcQuitty, 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


