We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...
Given its importance, the problem of predicting rare classes in large-scale multi-labeled data sets has attracted great attentions in the literature. However, the rare-class probl...
Traditional clustering algorithms work on "flat" data, making the assumption that the data instances can only be represented by a set of homogeneous and uniform features...
Levent Bolelli, Seyda Ertekin, Ding Zhou, C. Lee G...
Abstract. Integrative mining of heterogeneous data is one of the major challenges for data mining in the next decade. We address the problem of integrative clustering of data with ...
— Heterogeneous genome-wide data sources capture information on various aspects of complex biological systems. For instance, transcriptome, interactome and phenome-level informat...