Multidimensional Scaling
Springer texts in statisticsPublished 1 January 1986
S. H. C. du Toit, A. G. W. Steyn, R. H. Stumpf
Citations32
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
Abstract
A data matrix usually contains too much information to absorb at once. The differences between the various rows and columns as well as the interactions between them, are difficult to determine merely by looking at the matrix. However, if the above information can be simplified to representations in one, two or three dimensions at the most, the human eye is usually capable of observing any differences and interactions between rows and columns with the aid of geometrical distance comparisons. This simplifying process is commonly known as multidimensional scaling.
Keywords
Computer Science