We consider the problem of multiclass classification where both labeled and unlabeled data points are given. We introduce and demonstrate a new approach for estimating a distribut...
Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques f...
We consider clustering as computation of a structure of proximity relationships within a data set in a feature space or its subspaces. We propose a data structure to represent suc...
We study a number of open issues in spectral clustering: (i) Selecting the appropriate scale of analysis, (ii) Handling multi-scale data, (iii) Clustering with irregular backgroun...
Background: Predicting protein complexes from experimental data remains a challenge due to limited resolution and stochastic errors of high-throughput methods. Current algorithms ...
Wasinee Rungsarityotin, Roland Krause, Arno Sch&ou...