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

Authors

Qinglu Kong, University of Nebraska at Omaha
Qiuming Zhu, University of Nebraska at OmahaFollow

Document Type

Article

Publication Date

11-2007

Publication Title

Data & Knowledge Engineering

Volume

63

Issue

2

First Page

457

Last Page

477

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.

Comments

The final published version of this article can be found here: doi:10.1016/j.datak.2007.03.006.

© 2007. This manuscript version is made available under the CC-BY-NC-ND 4.0 licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/

Recommended Citation

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.
https://digitalcommons.unomaha.edu/compscifacpub/33