Pattern, Sequence and Anomaly Mining

Why this module belongs in the course

Association, sequence and anomaly methods discover candidates for investigation. They do not prove causation, fraud or intent without additional evidence.

Before you begin

The concepts and project evidence from Module 6. You should also be able to create a Python virtual environment and keep private or employer data out of the exercise.

Four lessons, one connected result

1. Lesson 1Association Rules, Apriori and FP-Growth95 min · Intermediate
2. Lesson 2Sequential Pattern Mining with GSP and PrefixSpan95 min · Intermediate
3. Lesson 3Outlier and Novelty Detection with LOF and Isolation Forest95 min · Intermediate
4. Lesson 4Project: Investigate Transaction Patterns and Anomalies170 min · Intermediate

How to know you are ready to continue

Complete the checkpoint without copying the worked example. Keep the code, output and a short decision note. Your note should explain one choice, one failure you observed and one limitation a reviewer should know.

Primary references for this module

The lessons explain the ideas in original wording. Use these primary or official sources when a library interface, standard or research claim needs verification.

• mlxtend association_rules
• mlxtend apriori
• mlxtend fpgrowth
• SPMF PrefixSpan documentation
• SPMF GSP documentation
• scikit-learn outlier detection
• scikit-learn IsolationForest
• scikit-learn LocalOutlierFactor