This article discusses the Apriori algorithm, which is used for mining repeating patterns in transaction datasets. It is used for finding frequent items and association between different items. The article explains the algorithm step by step and provides an example of how it works. It also discusses the confidence level and how it is used to generate association rules. The article concludes by discussing the efficiency of the algorithm and its applications.

Apriori algorithm is used for mining repeating patterns in transaction datasets. It is used for finding frequent items and association between different items. Learn more about it here.

Apriori Algorithm for Finding Frequent Itemsets and Association Rules - Desklib

Apriori Algorithm for Finding Frequent Itemsets and Association Rules

This assignment involves analyzing car buying decisions using a decision tree generated with the J48 algorithm in WEKA. The dataset contains attributes such as buying price, maintenance cost, doors, persons, luggage boot capacity, and safety rating.  The analysis includes generating a decision tree model, evaluating its performance with metrics like Kappa statistic, Mean Absolute Error, and Root Mean Squared Error, and interpreting the findings.

Apriori Algorithm for Finding Frequent Itemsets and Association Rules

Car Buying Decision Tree Analysislg...

Car Buying Decision Tree Analysis