PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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).

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.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Theory & Algorithms
ID Code:7961
Deposited By:Kai Puolamäki
Deposited On:17 March 2011