1 / 11

Chapter 11

Chapter 11. Multivariate Data, Multidimensional Space, and Spatialization. By Martin Bartnes. Chapter 11. Multivariate Data and Multidimensional Space Distance, Difference, and Similarity Cluster Analysis Spatialization Reducing the Numbers of Variables.

kraker
Download Presentation

Chapter 11

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chapter 11 Multivariate Data, Multidimensional Space, and Spatialization By Martin Bartnes

  2. Chapter 11 • Multivariate Data and Multidimensional Space • Distance, Difference, and Similarity • Cluster Analysis • Spatialization • Reducing the Numbers of Variables

  3. Multivariate Data and Multidimensional Space • Multivariate data are data where there is more than one item recorded for each observation.

  4. One, Two and Three Variables in a Single Plot

  5. Distance, Difference, and Similarity • Distance is a measure of the difference between pairs of observations. • The Pythagoras’s theorem:

  6. Cluster Analysis: • Cluster is a set of observations that are similar to each other and relatively different from other set of observations. • Cluster analysis can help to identify potential classifications in statistical data.

  7. Simple Cluster Technique

  8. Hierarchical Cluster Analysis • Work by building a nested hierarchy clusters.

  9. Spatialization: Mapping Multivariate Data • Multidimensional data can not be visualizing in more than three dimensions.

  10. Reducing the Number of Variables • Principal components analysis identifies a set of independent, uncorrelated variates, called the principal components. • Factor analysis is looking for hidden factors and attempt to identify them.

  11. Questions!!! • What are the main challenges with multidimensional data? • What are the point of cluster analysis?

More Related