Connecting Segments for Visual Data Exploration and Interactive Mining of Decision Rules

Abstract

Visualization has become an essential support throughout the KDD process in order to extract hidden information from huge amount of data. Visual data exploration techniques provide the user with graphic views or metaphors that represent potential patterns and data relationships. However, an only image does not always convey high–dimensional data properties successfully. From such data sets, visualization techniques have to deal with the curse of dimensionality in a critical way, as the number of examples may be very small with respect to the number of attributes. In this work, we describe a visual exploration technique that automatically extracts relevant attributes and displays their ranges of interest in order to support two data mining tasks: classification and feature selection. Through different metaphors with dynamic properties, the user can re-explore meaningful intervals belonging to the most relevant attributes, building decision rules and increasing the model accuracy interactively