# What are the steps of Apriori algorithm?

## What are the steps of Apriori algorithm?

Steps of the Apriori algorithm

- Computing the support for each individual item. The algorithm is based on the notion of support.
- Deciding on the support threshold.
- Selecting the frequent items.
- Finding the support of the frequent itemsets.
- Repeat for larger sets.
- Generate Association Rules and compute confidence.
- Compute lift.

### What is Apriori algorithm with example?

Apriori algorithm refers to an algorithm that is used in mining frequent products sets and relevant association rules. Generally, the apriori algorithm operates on a database containing a huge number of transactions. For example, the items customers but at a Big Bazar.

**What is Apriori algorithm in machine learning?**

Apriori is an algorithm used for Association Rule Mining. It searches for a series of frequent sets of items in the datasets. It builds on associations and correlations between the itemsets. It is the algorithm behind “You may also like” where you commonly saw in recommendation platforms.

**How do you calculate confidence A → B?**

Confidence(A→B) = Probability(A & B) / Support(A)Note, confidence is the same as what is also known as conditional probability in statistics: P(B|A) = P(A & B) / P(A) Please beware of the notation.

## What is Apriori algorithm PDF?

Apriori algorithm is a sequence of steps to be followed to find the most frequent itemset in the given database. This data mining technique follows the join and the prune steps iteratively until the most frequent itemset is achieved. A minimum support threshold is given in the problem or it is assumed by the user.

### How is Apriori algorithm used in daily life?

Apriori Algorithm usually contains or deals with a large number of transactions. For example, customers buying a lot of goods from a grocery store, by applying this method of the algorithm the grocery stores can enhance their sales performance and could work effectively.

**Why do we use Apriori algorithm?**

The Apriori algorithm is used for mining frequent itemsets and devising association rules from a transactional database. The parameters “support” and “confidence” are used. Support refers to items’ frequency of occurrence; confidence is a conditional probability. Items in a transaction form an item set.

**Is Apriori supervised or unsupervised?**

Apriori is generally considered an unsupervised learning approach, since it’s often used to discover or mine for interesting patterns and relationships. Apriori can also be modified to do classification based on labelled data.

## How do you calculate support in Apriori algorithm?

Minimum support count is the % of the all transaction. suppose you have 60% support count and 5 is the total transaction then in number the min_support will be 5*60/100=3.

### Is apriori supervised or unsupervised?

**Why is Apriori algorithm important?**

Importance of Apriori Algorithm Eases the construction of user interests. Identifies the importance of different itemsets. The support function helps to identify different types of importance in itemsets. Storage space is reduced with the help of unnecessary itemset reduction.

**Why is Apriori best?**

Why use Apriori? Apriori is well understood, easy to implement and has many derivatives. The algorithm can be quite memory, space and time intensive when generating itemsets.