When you talk about data mining, the discussion would not complete without mentioning the term “Apriori Algorithm”. In this blog, Let's know the work of Apriori Algorithm in Data Mining Projects. It is the process of sorting through large data sets to determine patterns and establish relationships to solve problems via data analysis. Data mining tools enable enterprises to predict future trends.

Apriori Algorithm

It is a classical algorithm which attempts to operate on database records, specifically transactional records or records including the specified number of items and fields. It is useful in frequent mining itemsets and relevant association rules. Naturally, when we operate this algorithm on a database containing a large number of transactions. It is using a bottom-up approach to contrast complex records incrementally and is useful in today's sophisticated machine learning and data mining projects. In fact, it works on two primary principles such as, if an itemset occurs frequently occurs then all subset of itemset occurs frequently, and other is that, if an itemset occurs infrequently then all superset has infrequently occurrences.

Applications in Apriori Algorithm

The algorithm is efficient for market basket analysis and helps to enhance market sale by assisting customers during the purchase of the item. Another popular application is Google Autocomplete in which the search engine suggests the other associated words according to your specified word, and also it is used in the Amazon recommendation system in Cloud Computing Projects.

As a matter of fact, it uses the result of applying this algorithm to sales data obtained from a large database company that shows the effectiveness of the Apriori algorithm. Apriori algorithm is to identify frequent itemsets to association between different itemsets i.e., association rule mining algorithm. For example, considers Big Data Projects and tries to obtain the Apriori algorithm can be additionally used and optimized. The main aim of Association rule mining algorithms is used to find out the best combination of different attributes in data.

Pros of Apriori Algorithm

• Simple and easy to understand algorithm among association rule learning algorithms
• This algorithm is exhaustive, so it finds all the rules with specified confidence and support
• The Resulting rules are intuitive and easy to communicate with an end user
• It does not require labeled data as it is fully unsupervised. Therefore, you can use it in several situations because unlabeled data is more accessible
• Several extensions were proposed for different use cases depend on this implementation