Preface to First Edition - lib

CLUSTER ANALYSIS 319x 20 2 4 6 8 100 5 10 15 20x 1Figure 18.1Bivariate data showing the presence of three clusters.ing procedures: agglomerative hierarchical clustering, k-means clustering andclassification maximum likelihood methods for clustering.18.2.1 Agglomerative Hierarchical ClusteringIn a hierarchical classification the data are not partitioned into a particularnumber of classes or clusters at a single step. Instead the classification consistsof a series of partitions that may run from a single ‘cluster’ containing allindividuals, to n clusters each containing a single individual. Agglomerativehierarchical clustering techniques produce partitions by a series of successivefusions of the n individuals into groups. With such methods, fusions, oncemade, are irreversible, so that when an agglomerative algorithm has placedtwo individuals in the same group they cannot subsequently appear in differentgroups. Since all agglomerative hierarchical techniques ultimately reduce thedata to a single cluster containing all the individuals, the investigator seeking© 2010 by Taylor and Francis Group, LLC