Software Engineering White Papers
Clustering Large Databases With Numeric and Nominal Values Using Orthogonal Projections
Overview Clustering large high-dimensional databases has emerged as a challenging research area. A number of recently developed clustering algorithms have focused on overcoming either the "Curse of dimensionality" or the scalability problems associated with large amounts of data. The majority of these algorithms operate only on numeric data, a few handle nominal data, and very few can deal with both numeric and nominal values. Orthogonal partitioning Clustering (O-Cluster) was originally introduced as a fast, scalable solution for large multidimensional databases with numeric values. Here, the paper extends O-Cluster to domains with nominal and mixed values. O-Cluster uses a top-down partitioning strategy based on orthogonal projections to identify areas of high density in the input data space.
| Publisher | Oracle | File Format | |
|---|---|---|---|
| Date Published | November 2004 | Downloads | 2 |
| Format | White Papers | ||
| Topics | |||
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