Abstract-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.
Visually controllable data mining This work was motivated by and can be considered an instance of visually controllable data mining REF , where the objective is to implement advanced data analysis method so that they are understandable and e ciently controllable by the user.
Visually Controllable Data Mining Methods
2010 IEEE International Conference on Data Mining Workshops
2010 IEEE International Conference on Data Mining Workshops
Computer Science