Method and apparatus for scalable probabilistic clustering using decision trees
US6564197B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 3, 1999 |
| Grant date | May 13, 2003 |
| Priority date | — |
| Expiry date | May 3, 2019 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY10S707/99936
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Some embodiments of the invention include methods for identifying clusters in a database, data warehouse or data mart. The identified clusters can be meaningfully understood by a list of the attributes and corresponding values for each of the clusters. Some embodiments of the invention include a method for scalable probabilistic clustering using a decision tree. Some embodiments of the invention, perform linearly in the size of the set of data and only require a single access to the set of data. Some embodiments of the invention produce interpretable clusters that can be described in terms of a set of attributes and attribute values for that set of attributes. In some embodiments, the cluster can be interpreted by reading the attribute values and attributes on the path from the root node of the decision tree to the node of the decision tree corresponding to the cluster. In some embodiments, it is not necessary for there to be a domain specific distance function for the attributes. In some embodiments, a cluster is determined by identifying an attribute with the highest influence on the distribution of the other attributes. Each of the values assumed by the identified attribute corr…
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