Unsupervised prioritization and visualization of clusters
US9659087B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 14, 2013 |
| Grant date | May 23, 2017 |
| Priority date | — |
| Expiry date | Oct 22, 2034 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F16/358
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are disclosed that automatically identify and order the most differentiated clusters from a given collection of clusters within a dataset. A measure of dissimilarity is computed for each cluster from a defined reference cluster, and the clusters are ordered according to the chosen dissimilarity. At least N clusters are selected as the most differentiated clusters relative to the defined reference. Within each cluster, the top-M most distinguishing cluster attributes can be automatically identified by an analogous process that computes the dissimilarity of each cluster attribute to its corresponding attribute in the reference cluster, and orders the attributes by dissimilarity. This then allows for automatic surfacing of what it is about a cluster that differentiates its members relative to the population as a whole, and to provide insight on what action or treatment might be made to address that specific segment of the underlying population.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.