Relationship mining reveals meaningful patterns and associations within datasets through three key techniques. Correlation mining quantifies statistical relationships between variables using metrics like Pearson's coefficient, indicating how changes in one variable relate to another without implying causation. Sequential Pattern Mining (SPM) identifies frequently occurring ordered sequences of events across time-series data, essential for predicting behaviors in applications from customer purchases to website navigation. Association Rule Mining (ARM) discovers co-occurrence relationships as "if-then" rules, measuring strengths through support, confidence, and lift metrics to reveal product affinities and purchasing patterns. These complementary approaches form a powerful toolkit for uncovering hidden data relationships that drive strategic decision-making in business intelligence and recommendation systems.
Relationship Mining Module Useful Links:
Relationship Mining Module Badge Activity
Relationship Mining Module Readings
Relationship Mining Basics
Module 1 - Correlation Mining:
Correlation Mining Conceptual Overview
Correlation Mining Case Study
Correlation Mining Code Along
Module 2 - Association Rule Mining:
Association Rule Mining Conceptual Overview
Association Rule Mining Case Study
Association Rule Mining Code Along
Module 3 - Sequential Pattern Mining:
Sequential Pattern Mining Conceptual Overview
Sequential Pattern Mining Case Study
Sequential Pattern Mining Code Along
ASSISTments Activity:
Correlation Mining:
Dataset1
Dataset2