# High-Dimensionality Clustering Algorithm

A High-Dimensionality Clustering Algorithm is a Clustering Algorithm for High-Dimensionality Record Sets.

**Example(s):****See:**Subspace Clustering Algorithm, Dimensionality Reduction Algorithm.

## References

### 1999

- (Agrawal et al., 1999) ⇒ Rakesh Agrawal, Johannes Ernst Gehrke, Dimitrios Gunopulos, Prabhakar Raghavan. (1999). “Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications." US Patent 6,003,029,
- Emerging data mining applications place special requirements on clustering techniques, such as the ability to handle
**high dimensionality**, assimilation of cluster descriptions by users, description minimation, and scalability and usability. Regarding high dimensionality of data clustering, an object typically has**dozens of attributes**in which the domains of the attributes are large. Clusters formed in a high-dimensional data space are not likely to be meaningful clusters because the expected average density of points anywhere in the high-dimensional data space is low. The requirement for**high dimensionality**in a data mining application is conventionally addressed by requiring a user to specify the subspace for cluster analysis.

- Emerging data mining applications place special requirements on clustering techniques, such as the ability to handle