Density-based indexing method for efficient execution of high dimensional nearest-neighbor queries on large databases
US6263334A · kind A · utility
Assignee
Inventors
Key dates
| Filing date | Nov 11, 1998 |
| Grant date | Jul 17, 2001 |
| Priority date | — |
| Expiry date | Nov 11, 2018 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY10S707/99943
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Method and apparatus for efficiently performing nearest neighbor queries on a database of records wherein each record has a large number of attributes by automatically extracting a multidimensional index from the data. The method is based on first obtaining a statistical model of the content of the data in the form of a probability density function. This density is then used to decide how data should be reorganized on disk for efficient nearest neighbor queries. At query time, the model decides the order in which data should be scanned. It also provides the means for evaluating the probability of correctness of the answer found so far in the partial scan of data determined by the model. In this invention a clustering process is performed on the database to produce multiple data clusters. Each cluster is characterized by a cluster model. The set of clusters represent a probability density function in the form of a mixture model. A new database of records is built having an augmented record format that contains the original record attributes and an additional record attribute containing a cluster number for each record based on the clustering step. The cluster model uses a probabilit…
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