The pros of Apriori are as follows:
- This is the most simple and easy-to-understand algorithm among association rule learning algorithms
- The resulting rules are intuitive and easy to communicate to an end user
- It doesn't require labeled data as it is fully unsupervised; as a result, you can use it in many different situations because unlabeled data is often more accessible
- Many extensions were proposed for different use cases based on this implementation—for example, there are association learning algorithms that take into account the ordering of items, their number, and associated timestamps
- The algorithm is exhaustive, so it finds all the rules with the specified support and confidence
The cons of Apriori are as follows:
- If the dataset is small, the algorithm can find many false associations that happened simply by chance. You can address this issue by evaluating obtained rules on the held-out test data for the support, confidence, lift, and conviction values.
- As Agrawal...