Market Basket Analysis is a powerful technique used in data mining and retail analytics to uncover relationships between items frequently purchased together by customers. This project implements the FP Growth algorithm in C to efficiently mine frequent itemsets from transaction data and discover association rules. The algorithm avoids the costly generation of candidate itemsets used in traditional approaches like Apriori, making it faster and more scalable.
The aim of this project is to provide a simple yet effective implementation of the FP Growth algorithm that can be easily used to analyze transaction datasets, identify frequent itemsets, and generate association rules.
FP Growth algorithm implementation in C for efficient market basket analysis. Support for handling large transaction datasets. Identification of frequent itemsets based on a user-defined minimum support threshold. Generation of association rules with customizable minimum confidence threshold. Easy-to-understand code with comments for better understanding and extension. Sample transaction dataset from Kaggle provided for testing and experimentation.
The Market Basket Analyzer uses the FP Growth algorithm to mine frequent itemsets. FP Growth stands for Frequent Pattern Growth, and it's a popular algorithm for finding frequent itemsets in transaction data. The algorithm utilizes a compact data structure called an FP-tree, which allows for efficient pattern mining without generating candidate itemsets.
For more details on the FP Growth algorithm, refer to the research paper by Han, Jiawei, Jian Pei, and Yiwen Yin.
The FP Growth algorithm research paper by Han, Pei, and Yin for the foundational knowledge. Kaggle for providing the sample transaction dataset used for testing. We hope that the Market Basket Analyzer using FP Growth Algorithm in C proves to be helpful for your market basket analysis tasks. If you have any questions or need assistance, please feel free to reach out.
Happy mining and analyzing!