Visual analytics of set data for knowledge discovery and member selection support

TL;DR

This paper introduces a visual analytics method for set data that models set generation processes with a manifold network, aiding knowledge discovery and member selection, demonstrated through basketball team analysis.

Contribution

The paper proposes a novel VA method for set data using manifold networks and topographic maps, addressing combinatorial challenges and enabling flexible, application-specific system development.

Findings

Effective visualization of set data using topographic maps.

Application to basketball team analysis demonstrates practical utility.

Method outperforms benchmark in outcome prediction and lineup reconstruction.

Abstract

Visual analytics (VA) is a visually assisted exploratory analysis approach in which knowledge discovery is executed interactively between the user and system in a human-centered manner. The purpose of this study is to develop a method for the VA of set data aimed at supporting knowledge discovery and member selection. A typical target application is a visual support system for team analysis and member selection, by which users can analyze past teams and examine candidate lineups for new teams. Because there are several difficulties, such as the combinatorial explosion problem, developing a VA system of set data is challenging. In this study, we first define the requirements that the target system should satisfy and clarify the accompanying challenges. Then we propose a method for the VA of set data, which satisfies the requirements. The key idea is to model the generation process of sets and their outputs using a manifold network model. The proposed method visualizes the relevant factors as a set of topographic maps on which various information is visualized. Furthermore, using the topographic maps as a bidirectional interface, users can indicate their targets of interest in the system on these maps. We demonstrate the proposed method by applying it to basketball teams, and compare with a benchmark system for outcome prediction and lineup reconstruction tasks. Because the method can be adapted to individual application cases by extending the network structure, it can be a general method by which practical systems can be built.