Abstract. We address the problem of selecting a subset of the most relevant features from a set of sample data in cases where there are multiple (equally reasonable) solutions. In ...
-- 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...
The availability of SiGe HBT devices has opened a door for Gigahertz FPGAs. However, the large device power consumption limits its scale. In order to solve this problem, a Multipl...
Jong-Ru Guo, Chao You, Michael Chu, Kuan Zhou, You...
Graph clustering has become ubiquitous in the study of relational data sets. We examine two simple algorithms: a new graphical adaptation of the k-medoids algorithm and the Girvan...
In the universal DNA chip method, target RNAs are mapped onto a set of DNA tags. Parallel hybridization of these tags with an indexed, complementary antitag array then provides an ...
John A. Rose, Russell J. Deaton, Masami Hagiya, Ak...