Generalized Clustering for Problem Localization

Abstract

The general procedure of conventional clustering is modified for various applications involving problem localization. This modification introduces the concept of clustering criteria which are used for partitioning a training set, and depend upon certain a priori information with regards to the training set. Also, the need of a structure or a mathematical form for the partition boundaries arises naturally from the need to process unknown samples. The general procedure is discussed in detail for applications of piecewise linear classifier design and piecewise linear density estimation. Experimental results are presented for both applications.