What This Document Is
This document provides sample solutions for Notebook 2 of the Computing for Data Analysis (CSE 6040) course at Georgia Tech. The notebook focuses on implementing a basic pairwise association rule mining algorithm, initially applied to a simplified text dataset (letters within words) and then extended to grocery store market basket data.
Why This Document Matters
This resource is intended for students enrolled in CSE 6040 who are working on Notebook 2. It serves as a reference for understanding potential approaches to solving the assignment problems and verifying their own implementations. It’s most useful when students are actively engaged in completing the notebook and need to check their work or gain insight into alternative solutions.
Common Limitations or Challenges
This document offers *solutions* but does not provide a detailed walkthrough of the underlying concepts of association rule mining. It assumes a foundational understanding of the problem definition and the goal of calculating confidence as a measure of conditional probability. It does not replace the need to understand the code and the reasoning behind it.
What This Document Provides
The full document includes:
* Completed code implementations for the letter-based association rule mining example.
* Code adapted for application to grocery store market basket data.
* Illustrative examples using a sample Latin text fragment.
* An ungraded exercise prompting exploration of the "Lorem Ipsum" text.
This preview does *not* include the complete code solutions, detailed explanations of the algorithms, or the results of applying the code to the datasets. It only provides a high-level overview of the notebook's content.
