chapter  23
Visual Classification
ByGiorgio Maria Di Nunzio
Pages 26

Extracting meaningful knowledge from very large datasets is a challenging task that requires the application of machine learning methods. This task is called data mining, the aim of which is to retrieve, explore, predict, and derive new information from a given dataset. Given the complexity of the task and the size of the dataset, users should be involved in this process because, by providing adequate data and knowledge visualizations, the pattern recognition capabilities of the human can be used to drive the learning algorithm [6]. This is the goal of Visual Data Mining [78, 85]: to present the data in some visual form, allowing the human to get insight into the data, draw conclusions, and directly interact with the data [18]. In [75], the authors define visual data mining as “the process of interaction and analytical reasoning with one or more visual representations of an abstract data that leads to the visual discovery or robust patterns in these data that form the information and knowledge utilised in informed decision making.”