Clustering with mixtures of bayesian networks
US6345265B1 · kind B1 · utility
Inventors
Key dates
| Filing date | Dec 23, 1998 |
| Grant date | Feb 5, 2002 |
| Priority date | — |
| Expiry date | Dec 23, 2018 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY10S707/99948
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The invention employs mixtures of Bayesian networks to perform clustering. A mixture of Bayesian networks (MBN) consists of plural hypothesis-specific Bayesian networks (HSBNs) having possibly hidden and observed variables. A common external hidden variable is associated with the MBN, but is not included in any of the HSBNs. The number of HSBNs in the MBN corresponds to the number of states of the common external hidden variable, and each HSBN is based upon the hypothesis that the common external hidden variable is in a corresponding one of those states. In one mode of the invention, the MBN having the highest MBN score is selected for use in performing inferencing. The invention determines membership of an individual case in a cluster based upon a set of data of plural individual cases by first learning the structure and parameters of an MBN given that data and then using the MBN to compute the probability of each HSBN generating the data of the individual case.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.