Physical Sciences and Mathematics Commons^™

Incremental Procedures For Partitioning Highly Intermixed Multi-Class Datasets Into Hyper-Spherical And Hyper-Ellipsoidal Clusters, Qinglu Kong, Qiuming Zhu

Computer Science Faculty Publications

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.