Visually controllable data mining methods
Kai Puolamäki, Panagiotis Papapetrou and Jefrey Lijffijt
In: IEEE ICDM Workshop on Visual Analytics and Knowledge Discovery — VAKD '10(2010).
A large number of data mining methods are, as
such, not applicable to fast, intuitive, and interactive use. Thus,
there is a need for visually controllable data mining methods.
Such methods should comply with three major requirements:
their model structure can be represented visually, they can
be controlled using visual interaction, and they should be fast
enough for visual interaction. We deﬁne a framework for using
data mining methods in interactive visualization. These data
mining methods are called “visually controllable” and combine
data mining with visualization and user-interaction, bridging
the gap between data mining and visual analytics. Our main
objective is to deﬁne the interactive visualization scenario and
the requirements for visually controllable data mining. Basic
data mining algorithms are reviewed and it is demonstrated
how they can be controlled visually. We also discuss how
existing visual analytics tools ﬁt to the proposed framework.
From a data mining perspective, this work creates a reference
framework for designing and evaluating visually controllable
algorithms and visual analytics systems.