Incremental procedures for partitioning highly intermixed multi-class datasets into hyper-spherical and hyper-ellipsoidal clusters

Authors:
Qinglu Kong;Qiuming Zhu
Affiliations:
Department of Computer Science, University of Nebraska at Omaha, Omaha, NE 68182-0050, USA;Department of Computer Science, University of Nebraska at Omaha, Omaha, NE 68182-0050, USA
Venue:
Data & Knowledge Engineering
Year:
2007

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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.