statsitical modelling (cluster analysis) project index statistical modelling (diagnostics)

English or languish - Probing the ramifications
of Hong Kong's language policy

CLUSTER ANALYSIS
Analytical Routines
cluster analysis (research design issues)

There are many clustering techniques available, and the researcher must decide among them.

Hierarchical Routines cluster analysis (identification | two-stage flow diagram)

Listed below are two opposite analytical approaches using the same statistical technique.

Both of these procedures are highly quantitative in so far as they minimize within group variance and require metric data that facilitates the calculation of means and variances. Both methods can be employed in either classification or structural modelling

Unlike other hierarchical clustering techniques this method uses an objective statistic trW where W is the pooled within-cluster sum of squares and cross-products matrix.The Ward's method is similar to the average linkage routines described below in so far as the minimized variance is a function of deviations from the sample mean. Ward's method assumes that the characteristic variable set is multivariate normally distributed.

Ward's Method is severely limited by its strong natural bias toward assigning similar numbers of observations to all clusters. Notwithstanding, because it is statistically rigorours, it makes an excellent starting point for the first stage of a two-stage clustering procedure.

Iterative Partitioning (Nonhierarchical) Methods
cluster analysis (two-stage flow diagram | key features)
These methods begin with the partioning of the observations into a specified number of clusters. The number of clusters can be determined on a random or nonrandom basis. Observations are then reassigned to clusters according to some stopping criterion. In addition to the stopping criterion non-hierarchical methods differ according to a number of different features including the

Other clustering techniques
cluster analysis (key features)

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