Decision Trees and Data Preprocessing to Help Clustering Interpretation
Olivier Parisot, Mohammad Ghoniem, Benoît Otjacques · 2014
Clustering is a popular technique for data mining, knowledge discovery and visual analytics. Unfortunately, clustering interpretation can be uneasy and this difficulty can be overcome by using decision trees to explain cluster assignment. In this work, we propose an evolutionary algorithm to preprocess the data in order to obtain clusters that are simpler to interpret with decision trees. A prototype has been implemented and tested to show the benefits of the approach.