Fast approximated relational and kernel clustering

Frank-Michael Schleif, Xibin Zhu, Andrej Gisbrecht, Barbara Hammer

Abstract: The large amount of digital data requests for scalable tools like efficient clustering algorithms. Many algorithms for large data sets request linear separability in an Euclidean space. Kernel approaches can capture the non-linear structure but do not scale well for large data sets. Alternatively, data are often represented implicitly by dissimilarities like for protein sequences, whose methods also often do not scale to large problems. We propose a single algorithm for both type of data, based on a batch approximation of relational soft competitive learning, termed fast generic soft-competitive learning. The algorithm has linear computational and memory requirements and performs favorable to traditional techniques <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .