Incremental constrained clustering with application to remote sensing images time series

Abstract

Automatically extracting knowledge from various datasets is a valuable task to help experts explore new types of data and save time on annotations. This is especially required for new topics such as emergency management or environmental monitoring. Traditional unsupervised methods often tend to not fulfill experts' intuitions or non-formalized knowledge. On the other hand, supervised methods tend to require a lot of knowledge to be efficient. Constrained clustering, a form of semi-supervised methods, mitigates these two effects, as it allows experts to inject their knowledge into the clustering process. However, constraints often have a poor effect on the result because it is hard for experts to give both informative and coherent constraints. Based on the idea that it is easier to criticize than to construct, this article presents a new method, I-SAMARAH, an incremental constrained clustering method. Through an iterative process, it alternates between a clustering phase where constraints are incorporated, and a criticize phase where the expert can give feedback on the clustering. We demonstrate experimentally the efficiency of our method on remote sensing image time series. We compare it to other constrained clustering methods in terms of result quality and to supervised methods in terms of number of annotations.