  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.
