Use of Computer Graphics and Cluster Analysis in Aiding Relational Judgment

Two experiments were conducted to investigate the use of computer graphics and cluster analysis in aiding human relational judgment. The experimental stimuli were similarity matrices from real-world data sources. The experimental tasks required subjects to detect the number of clusters or to judge the similarity between a designated pair of objects in the displayed matrix. Each matrix was either ordered randomly or arranged according to the results of cluster analysis. In the first experiment each matrix was displayed in one of four schemes: number scheme, color scheme, size scheme, or three-dimensional (3D) vertical line scheme. In the second experiment the matrices were displayed as 3D vertical lines with or without surface contour. The results indicated that the cluster-ordered displays greatly facilitated cluster detection performance in all but the 3D line scheme. Matrix ordering had no effect on the value judgment task. The perceptual compatibility principle and theories of perceptual grouping provided predictions and were tested in this experiment.

[1]  Desire L. Massart,et al.  The Interpretation of Analytical Chemical Data by the Use of Cluster Analysis , 1983 .

[2]  B. Everitt,et al.  An Attempt at Validation of Traditional Psychiatric Syndromes by Cluster Analysis , 1971, British Journal of Psychiatry.

[3]  F. Burbank.  A sequential space-time cluster analysis of cancer mortality in the United States: etiologic implications. , 1972, American journal of epidemiology.

[4]  Loy Anderson,et al.  Comparing Three-Dimensional Representations of Data to Scatterplots , 1987 .

[5]  G. Zorpette.  Technology '89: the main event , 1989, IEEE Spectrum.

[6]  S. C. Johnson.  Hierarchical clustering schemes , 1967, Psychometrika.

[7]  Arthur F. Kramer,et al.  Effects of Display Proximity and Memory Demands on the Understanding of Dynamic Multidimensional Information , 1986 .

[8]  Christopher D. Wickens,et al.  Comparative Graphics: History and Applications of Perceptual Integrality Theory and the Proximity Compatibility Hypothesis , 1988 .

[9]  William C. Halperin,et al.  Unclassed matrix shading and optimal ordering in hierarchical cluster analysis , 1984 .

[10]  Christopher D. Wickens,et al.  2-D vs. 3-D Display for Multidimensional Data Visualization: The Relationship between Task Integrality and Display Proximity , 1991 .

[11]  Herschel C. Hudson.  Classifying social data , 1982 .

[12]  L. Hubert,et al.  Hierarchical Clustering and the Concept of Space Distortion. , 1975 .

[13]  Walter D. Fisher,et al.  Clustering and Aggregation in Economics. , 1969 .

[14]  P. Sneath.  The application of computers to taxonomy. , 1957, Journal of general microbiology.

[15]  Karen A. Frenkel.  The art and science of visualizing data , 1988, CACM.

[16]  D. A. Duce,et al.  Visualization in Scientific Computing , 1994, Focus on Computer Graphics.

[17]  C.D. Wickens,et al.  Ergonomic design for perspective flight-path displays , 1989, IEEE Control Systems Magazine.

[18]  Mary Anne Buttigieg,et al.  Object Displays Do Not Always Support Better Integrated Task Performance , 1989 .

[19]  M. Braga,et al.  Exploratory Data Analysis , 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[20]  Vincent Kanade,et al.  Clustering Algorithms , 2021, Wireless RF Energy Transfer in the Massive IoT Era.

[21]  Christopher D. Wickens,et al.  Three Dimensional Display Technology for Aerospace and Visualization , 1990 .

[22]  Paul E. Green,et al.  Numerical Taxonomy in Marketing Analysis: A Review Article , 1968 .

[23]  B. King.  Market and Industry Factors in Stock Price Behavior , 1966 .

[24]  Robert F. Ling,et al.  A computer generated aid for cluster analysis , 1973, CACM.

[25]  Superconductor Images of Electron Devide Physics , 1990 .

[26]  Christopher D. Wickens,et al.  Proximity Compatibility and Information Display: Effects of Color, Space, and Objectness on Information Integration , 1990 .

[27]  Yili Liu,et al.  Visual Scanning with or without Spatial Uncertainty and Time-Sharing Performance , 1989 .

[28]  C Ware,et al.  Using Color Dimensions to Display Data Dimensions , 1988, Human factors.

[29]  G Kolata,et al.  Computer graphics comes to statistics. , 1982, Science.

[30]  Beat Kleiner,et al.  10 Graphical techniques for multivariate data and for clustering , 1982, Classification, Pattern Recognition and Reduction of Dimensionality.

[31]  B Julesz,et al.  Experiments in the visual perception of texture. , 1975, Scientific American.

[32]  Jean-Pierre Barthélemy,et al.  A model of selection by aspects , 1992 .

[33]  W. R. Garner,et al.  Interaction of stimulus dimensions in concept and choice processes , 1976, Cognitive Psychology.

[34]  P. Sneath,et al.  Numerical Taxonomy , 1962, Nature.

[35]  C D Wickens,et al.  Visual scanning with or without spatial uncertainty and divided and selective attention. , 1992, Acta psychologica.