Method and apparatus for reducing the computational requirements of K-means data clustering
US5983224A · kind A · utility
Assignee
Inventors
Key dates
| Filing date | Oct 31, 1997 |
| Grant date | Nov 9, 1999 |
| Priority date | — |
| Expiry date | Oct 31, 2017 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY10S707/99936
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present invention is directed to an improved data clustering method and apparatus for use in data mining operations. The present invention determines the pattern vectors of a k-d tree structure which are closest to a given prototype cluster by pruning prototypes through geometrical constraints, before a k-means process is applied to the prototypes. For each sub-branch in the k-d tree, a candidate set of prototypes is formed from the parent of a child node. The minimum and maximum distances from any point in the child node to any prototype in the candidate set is determined. The smallest of the maximum distances found is compared to the minimum distances of each prototype in the candidate set. Those prototypes with a minimum distance greater than the smallest of the maximum distances are pruned or eliminated. Pruning the number of remote prototypes reduces the number of distance calculations for the k-means process, significantly reducing the overall computation time.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.