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Course fee and EPTO/APTO units differ for on-site training.Applied Clustering Techniques
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Duration: 2.0 days Course fee: $1,450 EPTO units: 2.8 CEUs: 1.2 | ||
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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.
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
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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 I: Essentials course.
- have completed a graduate-level course in statistics or the Statistics II: ANOVA and Regression course.
- have an understanding of matrix algebra.
Course Contents
Introduction to Clustering
- types of clustering
- similarity metrics
- graphical clustering aids
- initialization issues
- variable clustering
- cluster preprocessing
- hierarchical clustering methods
- k-means clustering
- nonparametric clustering
- fuzzy clustering
- determining the number of clusters
- cluster profiling
- scoring new observations
Software
This course addresses SAS/STAT.
Course Materials
Students receive a hardcopy of the course notes and, in some courses, can choose to take home a copy of the course data.
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