Applied Clustering Techniques - CLUS92

The course looks at the theoretical and practical implications of a wide array of clustering techniques currently available in SAS. The techniques considered include cluster preprocessing, variable clustering, k-nearest-neighbor clustering, k-means clustering, hierarchical clustering, and fuzzy clustering.

2 days - Classroom

Learn how to

• 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 should attend?

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

Prerequisites

• 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 Introduction to Statistics Using SAS®: ANOVA, Linear Regression and Logistic Regression course.
• have an understanding of matrix algebra.

Course Contents

Introduction to Clustering

• identifying types of clustering
• measuring similarity
• assessing multivariate normality
• using classification matrices

Preparation for Clustering

• using variable clustering for variable selection
• using graphical clustering aids
• making elongated clusters more spherical
• viewing the impact of input standardisation

Partitive Clustering

• performing k-means clustering
• outlining the advantages of nonparametric clustering

Hierarchical Clustering

• using hierarchical clustering methods

Assessing Clustering Results

• determining the number of clusters
• profiling a cluster solution
• scoring new observations

Canonical Discriminant Analysis (CDA) Plots

• introducing canonical discriminant analysis

Fuzzy Clustering

• performing fuzzy clustering using the FACTOR procedure
• interpreting the PROC FACTOR output in terms of fuzzy clustering membership

Software Addressed

This course addresses the following software product(s): SAS/STAT.