The Distance and Cluster Procedure

Introduction

SAS version 9 introduced the proc distance procedure. All previous versions of SAS used two programs (xmacro.sas and distnew.sas) to process a transposed cocitation matrix (input) to produce a distance matrix (output). The input to the cluster and multi-dimensional scaling analysis is a proximity matrix. The cocitation frequency counts matrix must be converted into a distance or similarity matrix. SAS version 9 created a new procedure, the distance procedure, to compute various measures of distance, dissimilarity, or similarity between the authors under investigation. The distance matrix is the input to the CLUSTER and MDS procedures.

There are many different ways of measuring inter-object similarity, including distance measures (proximity/difference between each pair of objects) and the correlation coefficient between a pair of objects. The higher cocitation frequencies between a pair of authors represent a higher level of cognitive linkages or similarities between them. In ACA, the cocitation frequency count matrix, correlation coefficient matrix, and distance matrix represent three different outputs in the same transformation process (see Table 1). Understanding input and output relations in the process helps us select the correct options in the distance and MDS procedures.