Relationship Mining
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Relationship mining reveals meaningful patterns and associations within datasets through three key techniques: Correlation Mining, Association Rule Mining, and Sequential Pattern Mining. These complementary approaches form a powerful toolkit for uncovering hidden data relationships that drive strategic decision-making in business intelligence and recommendation systems.
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Github |
Repository for Instructors |
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Posit Cloud | Workspace for Learners |
Module 1: Correlation Mining
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.
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Conceptual Overview |
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Code Along |
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Readings & Reflection |
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Case Study |
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Module Survey |
Module 2: Association Rule Mining
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.
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Conceptual Overview |
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Code Along |
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Readings & Reflection |
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Case Study |
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Module Survey |
Module 3: Sequential Pattern Mining
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.
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Conceptual Overview |
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Code Along |
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Readings & Reflection |
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Case Study |
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Module Survey |
Microcredential
The culminating activity for the Relationship Mining modules is designed to provide you some space for independent analysis of a self-identified data source. To earn your Relationship Mining Microcredential, you must demonstrate your ability to formulate a relevant research question for relationship mining, effectively manage and analyze data, and clearly communicate your key findings. Your primary goal for this analysis is to create a simple data product that illustrates key findings by applying the knowledge and skills acquired from the essential readings and case studies.
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