  Visual analytics (VA) is a visually assisted exploratory analysis approach in
which knowledge discovery is executed interactively between the user and
system. 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, showing how past teams are analyzed and how
new lineups are examined. 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.
