We present an algorithm, Hierarchical ISOmetric SelfOrganizing Map (H-ISOSOM), for a concise, organized manifold representation of complex, non-linear, large scale, high-dimension...
Meaningfully integrating massive multi-experimental genomic data sets is becoming critical for the understanding of gene function. We have recently proposed methodologies for integ...
—A variety of clustering algorithms have recently been proposed to handle data that is not linearly separable; spectral clustering and kernel k-means are two of the main methods....
— In this paper a clustering algorithm that learns the groups of synchronized spike trains directly from data is proposed. Clustering of spike trains based on the presence of syn...
-- Combination of multiple clusterings is an important task in the area of unsupervised learning. Inspired by the success of supervised bagging algorithms, we propose a resampling ...
Behrouz Minaei-Bidgoli, Alexander P. Topchy, Willi...