Synthetic Dataset Generation with Itemset-Based Generative Models
This work addresses data generation for transactional datasets, but it is incremental as it builds on existing models without introducing major innovations.
The paper tackled generating synthetic transactional datasets by proposing three intuitive and easy-to-implement generators based on existing itemset-based models, and assessed their quality using three methods to preserve original dataset structure, with results showing satisfactory performance.
This paper proposes three different data generators, tailored to transactional datasets, based on existing itemset-based generative models. All these generators are intuitive and easy to implement and show satisfactory performance. The quality of each generator is assessed by means of three different methods that capture how well the original dataset structure is preserved.