18 Questions
What does a scatter plot provide a first look at?
Clusters of points and outliers
What is the general trend mentioned regarding the unit prices of items sold at Branch 1 compared to Branch 2?
Branch 1 prices are lower
Which type of visualization technique creates m windows on the screen, one for each dimension?
Pixel-oriented visualization techniques
What kind of data visualization technique helps in finding interesting regions and suitable parameters for further quantitative analysis?
Worlds-within-Worlds
Which visualization technique treats each pair of values as a pair of coordinates and plots them as points in the plane?
Scatter plot
In which type of data visualization technique are the colors of pixels used to reflect corresponding values?
Pixel-Oriented Visualization Techniques
Which visualization method involves visualizing complex data and relations?
Cone Trees
'Uncorrelated Data' falls under which category of data correlation?
'Uncorrelated Data'
'Hierarchical visualization techniques' involve which of the following?
'Nested structures'
Qual es le scopo del Jaccard coefficient?
Mensurar le similaritate inter datos assimetricos binarios.
Lo que representa le dissimilaritate a un distanta de 0 in un matrix de dissimilaritate?
Le objects es completmente simil.
Qual es le scopo de standardisar datos numeric?
Facilitar le comparation inter datos differente.
Le Minkowski distance pote esser applicate a qual typo de datos?
Datos numeric con n dimensiones.
Que es le proprieta principal de un metrica de distanta?
Le distanta inter objetos positive e symmetrical.
Qual es un exemplo de measure de proximitate pro attributos nominal?
Simple matching method.
Qual es le scopo del Z-score in le standardisation de datos numeric?
Standardisar le datos numeric in unidades de deviation standard.
'Jaccard coefficient' e 'coherence' representa lo mesme. Que significa isto?
'Jaccard coefficient' e 'coherence' representa similar conceptos.
'Manhattan Distance' es un exemplo de que tipo de metrica?
'L1 norm' metrica con ordine h = 1.
Test your knowledge on partitioning the n-dimensional attribute space into 2-D subspaces, attribute value ranges into classes, and mapping dimensions appropriately. Includes topics like stacking subspaces, using important attributes at outer levels, and displaying multiple dimensions effectively.
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