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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.
#q A paper mill products two grade of paper viz., X & Y. Because of raw material restriction, it cannot produce more than 400 tons of grade X paper & 300 tons of grade Y paper in a
Dr. Stallter has been teaching basic statistics for many years. She knows that 80% of the students will complete the assigned problems. She has also determined that among those who
meaning,uses,shortcomings and drawbacks of vital statistics
Minimum volume ellipsoid is a term for ellipsoid of the minimum volume which covers some specified proportion of the set of multivariate data. It is commonly used to construct rob
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The probability distribution, f (x), of largest extreme can be given as The location parameter, α is the mode and β is the scale parameter. The mean, variance skewn
Hypergeometric distribution is t he probability distribution related with the sampling without replacement from the population of finite size. If the population comprises of r ele
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