FP Growth Algorithm Implementation

Abstract

Data mining is to discover and assess significant patterns from data, followed by the validation of these identified patterns. Data mining is the process to evaluate the data from different perceptions and summarizing it into valuable information. This summarized information consequently can be used to design business strategies to upsurge revenue, occasionally drive down costs, or both. The Apriori association algorithm is based on pre-computed frequent item sets and it has to scan the entire transaction log / dataset or database which will become a problem with large item sets. With FP trees, there is no necessity for candidate generation, unlike in the Apriori algorithm, and the frequently occurring item sets are discovered by just traversing the FP tree. This paper discusses the FP Tree concept and implements it using Java for a general social survey dataset. We use this approach to determine association rules that occur in the dataset. In this manner, we can establish relevant rules and patterns in any set of records. General Terms Associaiton, FP Growth Algorithm