FP-Growth Implementation Using Tries for Association Rule Mining

Abstract

Abstract. With the advent of technology the past few years have seen the rise in the field of data mining. Data mining generally considered as the process of extracting the useful information by finding the hidden and the non-trivial information out of large chunks of dataset. Now here comes the role of association rule mining which forms a crucial component of data mining. Many recent applications like market basket analysis, text mining etc. are done using this approach. In this paper we have discussed the novel approach to implement the FP Growth method using the trie data structure. Tries provide a special feature of merging the shared sets of data with the number of occurrences that were already registered as count. So this paper widely gives an idea about how the interesting patterns are generated from the large databases using association rule mining methodologies.

Exploratory Analysis With Association Rule Mining Algorithms in the Retail Industry

“…By effectively grouping the overlapping items, this algorithm can reduce the space and time complexity in the case and apply the strategies to the data in this format. Implementing FP-Growth using the tries data structure has thus increased the performance of association rule mining undoubtedly (Goel et al, 2017).…”