SDRDPy: An application to graphically visualize the knowledge obtained with supervised descriptive rule algorithms

TL;DR

This work addresses the lack of accessible visualization tools for supervised descriptive rule discovery (SDRD) by introducing SDRDPy, a Python-based desktop application. SDRDPy processes SDRD results from any algorithm through a unified contingency-table representation, enabling integrated analysis of data, rules, and quality measures such as $TPr$, $FPr$, $Conf$, and $WRAcc$. It supports multiple SDRD families and six algorithms, with an algorithm-agnostic visualization focus and exportability to reports. The tool provides both general and per-rule visualizations (e.g., dot plots and pyramid plots) and accommodates crisp and fuzzy SDRD outputs, including NMEEFSD and APRIORISD variants, facilitating expert interpretation and dissemination. Overall, SDRDPy offers a practical, extensible solution for visualization and analysis of SDRD results, with potential applicability to big data and streaming contexts.

Abstract

SDRDPy is a desktop application that allows experts an intuitive graphic and tabular representation of the knowledge extracted by any supervised descriptive rule discovery algorithm. The application is able to provide an analysis of the data showing the relevant information of the data set and the relationship between the rules, data and the quality measures associated for each rule regardless of the tool where algorithm has been executed. All of the information is presented in a user-friendly application in order to facilitate expert analysis and also the exportation of reports in different formats.

Figures (5)

• Figure 1: System Architecture.
• Figure 2: Overview of general info (NMEEF algorithm).
• Figure 3: Overview of general info (APRIORISD algorithm).
• Figure 4: Overview of a specific rule (NMEEFSD algorithm).
• Figure 5: Overview of a specific rule (APRIORISD algorithm).