Statistical and Machine Learning Methods for Analyzing Clusters and Detecting Anomalies
This course will teach you how to use various cluster analysis methods to identify possible clusters in multivariate data. Methods discussed include hierarchical clustering, k-means clustering, two-step clustering, and normal mixture models for continuous variables.
After taking this course, you will be able to:
- Conduct hierarchical cluster analysis and k-means clustering to identify clusters in multivariate data
- Use normal mixture models for clustering of continuous variables
- Interpret/diagnose the output of different clustering procedures
- Apply normalization of data appropriately in cluster analysis
- Identify the assignment of cases to clusters
- Determine how to apply a supervised learning algorithm to a classification problem for anomaly detection
- Apply and assess a clustering algorithm for identifying anomalies in the absence of labels
Who Should Take This Course
- Marketing analysts who need to cluster customer data as part of a market segmentation strategy;
- Computational biologists (e.g. for taxonomy);
- Environmental scientists (e.g. for habitat studies);
- IT specialists (e.g. in modeling web traffic patterns);
- Military and national security analysts (e.g. in automated analysis of intercepted communications).
- Hierarchical clustering – dendrograms
- Divisive vs. agglomerative methods
- Distance metrics
- Different linkage methods
- Single linkage as anomaly detector
- K-means Clustering
- Choosing number of clusters
Normal Mixture Model
- Finite mixture model
- Statistical models to identify constituent groups
- K-means cluster as a special case
- Using subsets of variables
- Different data types
- Cluster quality and robustness
We assume you are versed in statistics. This course assumes knowledge of supervised learning, and some multivariate data is needed, such as that provided in the following courses.
- Skill: Intermediate
- Credit Options: ACE, CAP, CEU
- Skill: Intermediate
- Credit Options: ACE, CAP, CEU
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Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software. In addition to assigned readings, this course also has an end of course data modeling project.
This course will use papers that will be made available electronically, and will also refer to sections from the book Cluster Analysis, 5th Edition, by Brian S. Everitt, Dr Sabine Landau, Dr Morven Leese, Dr Daniel Stahl.
This is a hands-on course. Participants will apply cluster methods algorithms to real data, and interpret the results, so software capable of doing cluster analysis is required. The model solutions for the assignments were developed in IBM SPSS Statistics and Latent Gold. In addition, we also provide solutions using R. Other possible choices include XLStat and Analytic Solver Data Mining.
Options for Credit and Recognition
ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for Graduate credit, 3 semester hours in statistics. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.
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