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with Keith McCormick
Learn how to use cluster analysis, association rules, and anomaly detection algorithms for unsupervised learning.
Clustering and association
What you should know
Using the exercise files
What is unsupervised machine learning?
Looking at the data with a 2D scatter plot
Understanding hierarchical cluster analysis
Running hierarchical cluster analysis
Interpreting a dendrogram
Methods for measuring distance
What is k-nearest neighbors?
How does k-means work?
Which variables should be used with k-means?
Interpreting a box plot
Running a k-means cluster analysis
Interpreting cluster analysis output
What does silhouette mean?
Which cases should be used with k-means?
Finding optimum value for k: k = 3
Finding optimum value for k: k = 4
Finding optimum value for k: k = 5
What the best solution?
Summarizing cluster means in a table
Traffic Light feature in Excel
Line graphs
How does HDBSCAN work?
An HDBSCAN example
Relating clusters to categories statistically
Relating clusters to categories visually
Running a multiple correspondence analysis
Interpreting a perceptual map
Using cluster analysis and decision trees together
A BIRCH/two-step example
A self organizing map example
The k = 1 trick
Anomaly detection algorithms
Using SOM for anomaly detection
One Class SVM
Intro to association rules and sequence analysis
Running association rules
Some association rules terminology
Interpreting association rules
Putting association rules to use
Comparing clustering and association rules
Sequence detection
Next steps