Data & Knowledge Engineering
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.
Kong, Qinglu and Zhu, Qiuming, "Incremental procedures for partitioning highly intermixed multi-class datasets into hyper-spherical and hyper-ellipsoidal clusters" (2007). Computer Science Faculty Publications. 33.