| |  | Yang, Di | Managing Discoveries in The Visual Analytics Process read moreAbstract: Visualization systems traditionally focus on graphical representation
of information. They tend not to provide integrated analytical
services that could aid users in tackling complex knowledge discovery
tasks. Users’ exploration in such environments is usually
impeded due to several problems: 1) valuable information is hard
to discover when too much data is visualized on the screen; 2) Users
have to manage and organize their discoveries off line, because
no systematic discovery management mechanism exists; 3) their
discoveries based on visual exploration alone may lack accuracy;
and 4)they have no convenient access to the important knowledge
learned by other users. To tackle these problems, it has been recognized
that analytical toolsmust be introduced into visualization systems.
In this paper, we present a novel analysis-guided exploration
system, called the Nugget Management System (NMS). It leverages
the collaborative effort of human comprehensibility and machine
computations to facilitate users’ visual exploration processes.
Specifically, NMS first helps users extract the valuable information
(nuggets) hidden in datasets based on their interests. Given that
similar nuggets may be rediscovered by different users, NMS consolidates
the nugget candidate set by clustering based on their semantic
similarity. To solve the problem of inaccurate discoveries,
localized data mining techniques are applied to refine the nuggets
to best represent the captured patterns in datasets. Visualization
techniques are then employed to present our collected nugget pool
and thus create the nugget view. Based on the nugget view, interaction
techniques are designed to help users observe and organize
the nuggets in a more intuitive manner and eventually faciliate
their sense-making process. We integrated NMS into XmdvTool, a
freeware multivariate visualization system. User studies were performed
to compare the users’ efficiency and accuracy in finishing
tasks on real datasets, with and without the help of NMS. Our user
studies confirmed the effectiveness of NMS. | 2007 |