Co‐occurrence patterns in diagnostic data. (12th April 2020)

• Record Type: Journal Article
• Authors: Piceno, Marie Ely; Rodríguez‐Navas, Laura; Balcázar, José Luis
• Other Names: Ventura Sebastian guestEditor.; Soda Paolo guestEditor.; González Alejandro Rodríguez guestEditor.
• Is Part Of: Computational intelligence. Volume 37:Number 4(2021)
• Page Start: 1499
• Page End: 1514
• Publication Date: 2020-04-12
• Subjects: clan decomposition -- exploratory data analysis -- Gaifman graphs; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12317
• Languages: English
• ISSNs: 0824-7935

Abstract

Summary: We demonstrate how graph decomposition techniques can be employed for the visualization of hierarchical co‐occurrence patterns between medical data items. Our research is based on Gaifman graphs (a mathematical concept introduced in Logic), on specific variants of this concept, and on existing graph decomposition notions, specifically, graph modules and the clan decomposition of so‐called 2‐structures. The construction of the Gaifman graphs from a dataset is based on co‐occurrence, or lack of it, of items in the dataset. We may select a discretization on the edge labels to aim at one among several Gaifman graph variants. Then, the decomposition of the graph may provide us with visual information about the data co‐occurrences, after which one can proceed to more traditional statistical analysis.