Biclustering performs simultaneous clustering on features and data. It automatically integrates feature selection to clustering without any prior information, so that the relations of clusters of unsupervised labels (for example, genes) and clusters of data (for example, samples or conditions) are established. Using neural based classifiers, this proprietary algorithm has been experimentally tested on the results of multiple human cancer data sets and has shown that it clusters structures with higher qualities than other commonly used algorithms.
This algorithm provides a distinct advantage over older techniques for firms that perform genetic analysis, data mining or search and sorting functions. Not only does this algorithm perform processes faster, it also uses less system resources while doing so.
-Lower memory footprint
-More appropriate for embedded or real-time systems
The Fast Biclustering Algorithm is perfectly suited to the fields of genetic analysis, data mining or even social networking analysis. Almost any application that involves sorting and searching through massive amounts of data will benefit from this technology. As networks continue to grow in size, complexity and ubiquity, more fields will develop data mounds of such scale that the only economical way to analyze them will be with smart programs like the Fast Biclustering Algorithm.
-Algorithm has been developed and successfully demonstrated