Synthetic Dataset Generation with Itemset-Based Generative Models

TL;DR

This paper introduces three new itemset-based generative models for creating synthetic transactional datasets, emphasizing simplicity and effectiveness, with evaluation methods to measure how well they preserve original data structures.

Contribution

The paper presents three novel, easy-to-implement data generators for transactional data based on itemset models, with comprehensive quality assessment methods.

Findings

All generators show satisfactory performance.

Evaluation methods effectively measure data structure preservation.

Generators are intuitive and easy to implement.

Abstract

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.