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K-means cluster analysis is the method of cluster analysis in which from an initial partition of observations into K clusters, each observation in turn is analysed and reassigned, if suitable, to a different cluster in an attempt to optimize some predefined numerical criterion that measures in some sense the 'quality' of cluster solution. Several such clustering criteria have been suggested, but the most usually used arise from considering the features of the within groups, between groups and whole matrices of sums of squares and the cross products (W, B, T) which can be described for every partition of the observations into the particular number of groups. The two most ordinary of the clustering criteria developing from these matrices are given as follows
minimization of trace W
minimization of determinant W
The first of these has tendency to produce the 'spherical' clusters, the second to produce clusters that all have same shape, though this will not necessarily be spherical in shape.
Baddeley'smetric : A manner of measuring the 'error' in the image processing technique or method. The metric is derived using the fundamental theory from the stochastic geometry an
Locally weighted regression is the method of regression analysis in which the polynomials of degree one (linear) or two (quadratic) are used to approximate regression function in
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This is an approach to the modelling of time-frequency surfaces which consists of a Bayesian regularization scheme in which the prior distributions over the time-frequency coeffici
Recursive models are the statistical models in which the causality flows in one direction, that is models which include only unidirectional effects. Such type of models do not inc
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MEANING ,IMPORTANCE AND RELEAVANCE OF SCATTER DIAGRAM
Likert scales is often used in the studies of attitudes in which the raw scores are based on the graded alternative responses to each of a series of queries. For instance, the sub
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