Interactive Machine Learning Tool for Clustering in Visual Analytics
Michael Christoph Thrun, Felix Pape, Alfred Ultsch · 2020
Clustering is an important task in knowledge discovery with the goal of finding groups of similar data points in a dataset. Today there are many different approaches to clustering, including methods to incorporate user decisions into the clustering process. Some of these interactive approaches fall into the category of visual analytics and emphasize the power of visualizations to help find clusters manually in various types of datasets or to verify the results of clustering algorithms. The interactive projection-based clustering (IPBC) is an open-source and parameter-free method using user input on interactive visualizations to cluster high-dimensional data. This work introduces the IPBC approach and compares it to the results of accessible visual analytics approaches for clustering, showing that IPBC can outperform them.