Oracle Database 19c: Data Mining Techniques — Lesson 3: Clustering, Anomaly Detection, Association Rules, and Segmentation

Learning Objectives

• Explain the architecture, workflow, or business purpose behind distance, similarity, cluster models, centroids, and profiles.
• Apply anomaly detection, rare cases, contamination, and review workflow and association rules, support, confidence, lift, and market-basket interpretation in a controlled scenario.
• Validate outcomes and troubleshoot risks involving segment naming, stability, actionability, and business validation.

Detailed Topics

• distance, similarity, cluster models, centroids, and profiles
• anomaly detection, rare cases, contamination, and review workflow
• association rules, support, confidence, lift, and market-basket interpretation
• segment naming, stability, actionability, and business validation

Instructor Demonstration

Demonstrate distance, similarity, cluster models, centroids, and profiles within a customer-risk dataset modeled and scored inside Oracle Database with transparent validation and deployment controls, then capture the exact configuration, command, transaction, report, model, query, code, or process evidence that proves the result for lesson 3.

Hands-On Exercise

Implement a controlled exercise covering anomaly detection, rare cases, contamination, and review workflow and association rules, support, confidence, lift, and market-basket interpretation, then introduce and resolve an exception involving segment naming, stability, actionability, and business validation while preserving before-and-after evidence.

1. Confirm the environment, identities, inputs, dependencies, risks, and expected outcome.
2. Complete the course-specific configuration, development, analysis, or process workflow.
3. Capture settings, commands, data, outputs, logs, reports, screenshots, calculations, or test results.
4. Compare the observed result with acceptance criteria and document corrective action.

Scenario Analysis

• Identify the technical or business decision that must be made.
• Compare valid approaches and their security, performance, reliability, cost, compliance, and support trade-offs.
• Define rollback, recovery, ownership, communication, and evidence requirements.

Troubleshooting Guidance

Diagnose issues across distance, similarity, cluster models, centroids, and profiles, anomaly detection, rare cases, contamination, and review workflow, association rules, support, confidence, lift, and market-basket interpretation, segment naming, stability, actionability, and business validation without making an unverified production change. Separate symptoms from causes, gather evidence, test the smallest reversible correction, rerun validation, and record preventive action.

Validation Checklist

• The requirement, scope, inputs, dependencies, and expected result are documented.
• The result is reproducible and supported by course-specific evidence.
• Normal, boundary, failure, security, performance, and operational conditions are reviewed.
• Exceptions, rollback, ownership, and the next improvement priority are recorded.

Lesson Outcome

Learners can apply Clustering, Anomaly Detection, Association Rules, and Segmentation in a realistic Oracle Database 19c: Data Mining Techniques scenario and validate the result with documented evidence.