PYRAMIDAL CLASSIFICATION BASED ON INCOMPLETE DISSIMILARITY DATA

Two algorithms for pyramidal classification - a generalization of hierarchical classification - are presented that can work with incomplete dissimilarity data. These approaches - a modification of the pyramidal ascending classification algorithm and a least squares based penalty method - are described and compared using two different types of complete dissimilarity data in which randomly chosen dissimilarities are assumed missing and the non-missing ones are subjected to random error. We also consider relationships between hierarchical classification and pyramidal classification solutions when both are based on incomplete dissimilarity data.