Apriori, Frequent Pattern (FP) Growth, Association Rules/Analysis
- Supervised Learning ... Semi-Supervised ... Self-Supervised ... Unsupervised
- Apriori Algorithm (Python 3.0)
- Mining Energy Consumption Behavior Patterns for Households in Smart Grid
- Apriori Algorithm for Association Rule Learning — How To Find Clear Links Between Transactions | Saul Dobilas - Medium ... Explanation and examples of frequent itemset mining and association rule learning over relational databases in Python
Apriori and FP-Growth are two popular algorithms used in association rule mining.
- Apriori is an algorithm that uses frequent itemsets to generate association rules. However, the Apriori algorithm repeatedly scans the database in the mining process, which influences the mining running speed and the average search space for all frequent itemset is large.
- FP-Growth (frequent-pattern growth) algorithm is an improved algorithm of the Apriori algorithm. It compresses data sets to a FP-tree, scans the database twice, does not produce the candidate item sets in mining process, and greatly improves the mining efficiency.
Association rules are used to find relationships between variables in large datasets. The goal of association rule mining is to find all rules that have support and confidence greater than or equal to a user-specified minimum support and confidence.