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A clustering technique for summarizing multivariate data
Author(s) -
Ball Geoffrey H.,
Hall David J.
Publication year - 1967
Publication title -
behavioral science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.371
H-Index - 45
eISSN - 1099-1743
pISSN - 0005-7940
DOI - 10.1002/bs.3830120210
Subject(s) - cluster analysis , multivariate statistics , cluster (spacecraft) , data mining , point (geometry) , data set , computer science , set (abstract data type) , mathematics , artificial intelligence , machine learning , geometry , programming language
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.