B1370
Title: Fast and interpretable consensus clustering via minipatch learning
Authors: Genevera Allen - Rice University (United States) [presenting]
Abstract: Consensus clustering has been widely used in bioinformatics and other applications to improve the accuracy, stability and reliability of clustering results. This approach ensembles cluster co-occurrences from multiple clustering runs on subsampled observations. For application to large-scale bioinofmratics data, consensus clustering has two significant drawbacks: computational inefficiency and lack of interpretability into important features for differentiating clusters. We address these two challenges by developing IMPACC: Interpretable MiniPatch Adaptive Consensus Clustering. We ensemble cluster co-occurrences from tiny subsets of both observations and features, termed minipatches, thus dramatically reducing computation time. Additionally, we develop adaptive sampling schemes for observations, which result in both improved reliability and computational savings, as well as adaptive sampling of features, which leads to interpretable solutions by quickly learning the most relevant features that differentiate clusters. We study our approach on synthetic data and a variety of real large-scale bioinformatics data sets; results show that our approach not only yields more accurate and interpretable cluster solutions, but also substantially improves computational efficiency compared to standard consensus clustering approaches.
