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Applied Clustering Techniques

The course looks at the theoretical and practical implications of a wide array of clustering techniques that are currently available in SAS. The techniques considered include cluster preprocessing,...

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$1,975 USD GSA  $1,552.44
Course Code CLUS51
Duration 2 days
Available Formats Classroom, Virtual
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The course looks at the theoretical and practical implications of a wide array of clustering techniques that are currently available in SAS. The techniques considered include cluster preprocessing, variable clustering, -means clustering, and hierarchical clustering.

Skills Gained

  • Prepare and explore data for a cluster analysis.
  • Distinguish among many different clustering techniques, making informed choices about which to use.
  • Evaluate the results of a cluster analysis.
  • Determine the appropriate number of clusters to retain.
  • Profile and describe clustered observations.
  • Score observations into clusters.

Who Can Benefit

  • Intermediate- or senior-level statisticians, data analysts, and data miners

Prerequisites

  • Before attending this course, you should:
  • Be able to execute SAS programs and create SAS data sets. You can gain this experience by completing the SAS® Programming 1: Essentials course.
  • Have completed a graduate-level course in statistics or the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course.
  • Have an understanding of matrix algebra.

Course Details

Introduction to Clustering

  • Overview.
  • Types of clustering in this course.

Preparation for Clustering

  • Sample selection.
  • Variable selection.
  • Variable standardization.
  • Graphical aids to clustering.
  • Within cluster variable transformation.

Hierarchical Clustering

  • Measuring similarity.
  • Hierarchical clustering methods.
  • Determining the number of clusters.
  • k
  • The -means clustering algorithm.
  • -means clustering using the FASTCLUS procedure.
  • Determining the number of clusters.

Nonparametric Clustering

  • Nonparametric clustering.
  • Practices.

Cluster Profiling and Scoring

  • Cluster profiling.
  • Scoring new observations.

Appendix A: Canonical Discriminant Analysis (CDA) Plots

Appendix B: Fuzzy Clustering

Appendix C: Assessing Multivariate Normality

Appendix D: References

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