Clustering Combination Method

Clustering combination uses more than one clustering method with identical pattern features to improve the clustering performance. In general clustering is an optimization procedure based on a specific clustering criterion, so clustering combination can be regarded as a technique that constructs and processes multiple clustering criteria rather than a single criterion. We propose two methods of combining objective function clustering and graph theory clustering. One incorporates multiple criteria into an objective function according to their importance, and solves this problem with constrained nonlinear optimization programming. The other method consists of two sequential procedures: (a) a traditional objective function Clustering for generating the initial result, and (b) an autoassociative additive system based on graph theory clustering for modifying the initial result.