Frequent itemset mining using graph theory / Mohammad Arsyad Mohd Yakop

There is a number of algorithms focusing on frequent itemsets mining (FIM) field, however, some of the problems still require attention, particularly when the mining process involves a high dimensional dataset. The Directed Acyclic Graph in High Dimensional Dataset Mining (DAGHDDM) is a graph-based mining algorithm that represents itemsets in complete graph before FIM takes place. Nevertheless, the creation of the complete graph creates unnecessary edges and make the search space large and affects the overall performance. This research aims to speed up the searching process by creating relevant edges in the graph to reduce the search space by rearranging the items using the common prefix rowset._We proposed a novel frequent itemset mining using a graph theory called Frequent Row Graph Closed (FRG-Closed). Designing the FRG-Closed involves new data structure creation known as Frequent Row Graph or FR-Graph. The searching process in the FR-Graph involves the construction of two methods: getPath and item-merging. Experiments were performed to compare the performance of FRG-Closed and Directed Acyclic Graph in High Dimensional Dataset Mining (DAGHDDM) algorithm. The result of the experiments revealed the FRG Closed capability to mine the frequent closed itemset faster than its counterpart, DAGHDDM algorithm. Moreover, the FRG-Closed is also able to handle lower minimum support compared to the DAGHDDM for a larger transaction.