Comparison of association rule mining algorithms- Apriori and FP growth

Abstract

Data mining is a part of a process called KDD-knowledge discovery in databases. This process consists basically of steps that are performed before carrying out data mining, such as data selection, data cleaning, pre-processing, and data transformation. Association rule techniques are used for data mining if the goal is to detect relationships or associations between specific values of categorical variables in large data sets. There may be thousands or millions of records that have to be read and to extract the rules. Frequent pattern mining is a very important task in data mining. The approaches applied to generate frequent set generally adopt candidate generation and pruning techniques for the satisfaction of the desired objectives. This dissertation shows how the different approaches achieve the objective of frequent mining along with the complexities required to perform the job. This dissertation looks into a comparison among Apriori and FP Growth algorithm. The process of the mining is helpful in generation of support systems for many computer related applications. It has been observed that with higher support and confidence on both algorithms, FP-Growth extracts the better association rules than Apriori algorithm. While decreasing the support and confidence value Apriori seems better than FP-Growth algorithm.