Methods and systems for biclustering algorithm
Methods and systems for improved unsupervised learning are described. The unsupervised learning can consist of biclustering a data set, e.g., by biclustering subsets of the entire data set. In an example, the biclustering does not include feeding know and proven results into the biclustering methodology or system. A hierarchical approach can be used that feeds proven clusters back into the biclustering methodology or system as the input. Data that does not cluster may be discarded. Thus, a very large unknown data set can be acted on to learn about the data. The system is also amenable to parallelization.
D. C. Wunsch et al., "Methods and systems for biclustering algorithm," The Curators of The University Of Missouri, May 2015.
Electrical and Computer Engineering
Center for High Performance Computing Research
Patent Application Number
© 2015 The Curators of the University of Missouri, All rights reserved.