Abstract
Two procedures for partitioning large collections of highly intermixed datasets of different classes into a number of hyper-spherical or hyper-ellipsoidal clusters are presented. The incremental procedures are to generate a minimum numbers of hyper-spherical or hyper-ellipsoidal clusters with each cluster containing a maximum number of data points of the same class. The procedures extend the move-to-front algorithms originally designed for construction of minimum sized enclosing balls or ellipsoids for dataset of a single class. The resulting clusters of the dataset can be used for data modeling, outlier detection, discrimination analysis, and knowledge discovery.
Original language | English (US) |
---|---|
Pages (from-to) | 457-477 |
Number of pages | 21 |
Journal | Data and Knowledge Engineering |
Volume | 63 |
Issue number | 2 |
DOIs | |
State | Published - Nov 2007 |
Externally published | Yes |
Keywords
- Data clustering
- Data models
- Geometrical approximation
- Knowledge discovery
- Mini-max partition
ASJC Scopus subject areas
- Information Systems and Management