A Comprehensive Review on Association Rule Mining and its Algorithms: Apriori, FP Growth, and SS-FIM

Abstract

The technique of extracting usable information from databases is known as data mining. It is employed to manage massive amounts of data. In order to forecast future trends and behaviours, data mining is performed. Today, data mining is a technique that is growing helpful since it has many uses. Massive amounts of data are produced daily in many different fields. Data mining is utilised since it is challenging to analyse such a big volume of data. It is often referred to as database knowledge discovery (KDD). Data mining technologies are used to forecast the behaviour and pattern of the data, assisting organisations in decision-making [1]. Association Rule Mining is one of the data mining techniques discussed in the present article. It has three algorithms. 1. Apriori, 2. FP-Growth, and 3. SS-FIM. In the current work, all three algorithms are discussed. After examining each algorithm, it was determined that the SS-FIM algorithm had the best combination of features