UNVEILING HIDDEN PATTERNS : EXPLORING THE POTENTIAL OF FREQUENT MINING ALGORITHM

Abstract

In the field of data mining, frequent pattern mining has become essential and is attracting a lot of interest from academics. This task's primary objective is to identify repeating subgroups within set sequences. These kinds of projects are important in many different data mining fields, including web mining, association rule discovery, classification, clustering, and market analysis. Many frameworks have been created all through time to make regular pattern mining easier, with the support-based approach being the most well-known. In order to work, the support-based framework looks for item sets that have a frequency threshold over which they fall. This cutoff is used as a standard to assess how important the patterns are in the dataset. Through the process of identifying frequently occurring item sets, analysts might uncover significant patterns and correlations within the data. This review paper explores multiple algorithms for frequent mining and provides brief explanations for each of them. Apriori, FP-Growth, and Eclat, three of the most popular techniques in the area, are among the algorithms examined. Every algorithm has a unique collection of benefits, limitations, and guiding ideas that enable it to be applied to many situations and datasets. After performing transaction aggregation on the dataset, the research concludes with a comparative examination of frequently used pattern mining approaches, in addition to examining each of these algorithms separately. This comparison analysis compares the algorithms based on a number of important factors, including memory consumption, computational complexity, scalability, and adaptability to various types of data. By studying these variables, researchers can find out more about the advantages and disadvantages of each strategy, which can help them choose the most effective method for a particular mining.