Prioritizing Effectiveness of Algorithms of Association Rule Mining

Abstract

From the last decade, clouds have become the most popular platform for data storage.  In the current age, people and machines are engaged in transferring data on clouds.  The devices like cameras, computers, mobile, and CCTV are being used to transmit data collectively on the clouds in each second. Such a huge collection of data on clouds is known as Big data.  Data mining is a process of extracting useful information from a set of huge data. The different techniques like Association Rule Mining, Classification, and Clustering are some of the well-known techniques, which can be used for data mining purposes. Association Rule Mining is a process of mining associations and correlations among the items in a large data set. Some traditional algorithms like Apriori, FP-growth, and Eclat are being used for association rule mining, but no one provides an optimal solution. In our study, we ascertained the working algorithms, evaluated their performance, and finally ranked them based on their efficiency. We adopted a quantitative approach in our research. We framed queries, pinpointed pertinent work, gauged quality, summarized the evidence, and finally interpreted our findings.