Closed Itemset Mining: A graph theory perspective (Informatica, 2024)
Data Mining is the field which targets the extraction and the analysis ofusable data from a large database. In this paper, we focus on the most studiedproblems in the field. That is finding closed frequent itemsets. Up to now,various graph theory techniques have been proposed to solve the frequentitemsets problem. Unfortunately, these techniques have the drawback ofneglecting the existing relationship between each pair of items that appearin the same transaction. In this paper, first of all, we present a scalable newmodeling approach which allows the representation of a transaction datasetby an undirected and labeled graph. The labels are astutely computed andproperly assigned to the bonds. Secondly, based on the clique notion ingraph theory, we propose polynomial and exact algorithm that computes allthe closed frequent itemsets. In terms of CPU-time and the memory usage,our first testing results show the efficiency of our algorithm compared torecent methods selected in the literature. Thus, the benefit of using ourgraph model is its ability to be simply extended when the correspondingdataset is updated.In other words, the proposed labeled graph incorporatesall the information contained in the transaction database. This is the strongaspect of the model, which can be used to investigate more challenging issuesrelated to the problem dealt within this paper.