A clustering technique for summarizing multivariate data

Abstract

Scientific measurements frequently involve large numbers of variables whose complex interactions are not easily found. A practical computing method termed ISODATA, which finds the cluster structure of such data, is described. The resulting description of the data provides a fit to the data of a set of cluster centers that tends to minimize the sum of the squared distances of each data point from its closest cluster center. An application to the grouping or clustering of the answers of 209 people to an 80-question sociological survey illustrates the utility of the method.