3 accepted papers
Data fusion (DF) from multiple heterogeneous sources is a typical task for many multisensor applications including remote sensing classification problems. Multiple classifier systems (MCS) provide a natural way to solve DF on the decision level by training individual classifiers separately on its ow…
The combination of multiple clustering solutions used to obtain accurate and novel output has attracted attention in data clustering research. Despite the success of clustering ensembles, there are still several fundamental limiting issues including the lack of a unified formalized problem formulati…
Consensus clustering, also known as clustering ensembles is a technique that combines multiple clustering solutions to obtain stable, accurate and novel results. Over the last years several consensus clustering approaches were proposed addressing practical clustering problems with different degrees…