DaaG: Visual Analytics Clustering Using Network Representation

Abstract

Finding useful patterns in datasets has attracted considerable interest in the field of visual analytics. One of the most common solutions is the identification and representation of clusters. In this work, we propose a visual analytics clustering methodology for guiding the user in the exploration and detection of clusters in a dataset. We thereby combine the homological algebra with a graphical representation of the clustered dataset as a network into one coherent framework. Our approach entails displaying the results of the heuristics to users, providing a setting from which to start the exploration and data analysis.