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Visual analytics (VA) is a visually assisted exploratory analysis approach inwhich knowledge discovery is executed interactively between the user and systemin a human-centered manner. The purpose of this study is to develop a methodfor the VA of set data aimed at supporting knowledge discovery and memberselection. A typical target application is a visual support system for teamanalysis and member selection, by which users can analyze past teams andexamine candidate lineups for new teams. Because there are severaldifficulties, such as the combinatorial explosion problem, developing a VAsystem of set data is challenging. In this study, we first define therequirements that the target system should satisfy and clarify the accompanyingchallenges. Then we propose a method for the VA of set data, which satisfiesthe requirements. The key idea is to model the generation process of sets andtheir outputs using a manifold network model. The proposed method visualizesthe relevant factors as a set of topographic maps on which various informationis visualized. Furthermore, using the topographic maps as a bidirectionalinterface, users can indicate their targets of interest in the system on thesemaps. We demonstrate the proposed method by applying it to basketball teams,and compare with a benchmark system for outcome prediction and lineupreconstruction tasks. Because the method can be adapted to individualapplication cases by extending the network structure, it can be a generalmethod by which practical systems can be built.

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