K-means cluster analysis, Advanced Statistics

Assignment Help:

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. 

 


Related Discussions:- K-means cluster analysis

Linear regression assignment help, Using World Bank (2004) World Developmen...

Using World Bank (2004) World Development Indicators; Washington: International Bank for Reconstruction & Development/ The World Bank, located in the reference section of the Learn

Homoscedasticity - reasons for screening data, Homoscedasticity - Reasons f...

Homoscedasticity - Reasons for Screening Data Homoscedasticity is the assumption that the variability in scores for a continuous variable is roughly the same at all values of

Unequal probability sampling, Unequal probability sampling is the sampling...

Unequal probability sampling is the sampling design in which the different sampling units in the population have different probabilities of being included in sample. The differing

Forecast, The particular projection which an investigator believes is most ...

The particular projection which an investigator believes is most likely to give an accurate prediction of the future value of some process. Commonly used in the context of the anal

Explain johnson-neyman technique, Johnson-Neyman technique:  The technique ...

Johnson-Neyman technique:  The technique which can be used in the situations where analysis of the covariance is not valid because of the heterogeneity of slopes. With this method

Factorial designs, Designs which permits two or more questions to be addres...

Designs which permits two or more questions to be addressed in the investigation. The easiest factorial design is one in which each of the two treatments or interventions are p

Data theory, Data theory is anxious with how observations are transformed i...

Data theory is anxious with how observations are transformed into data which can be analyzed. Data are thus viewed as the theory laden in the sense that the observations can be giv

Describe hello-goodbye effect., Hello-goodbye effect : The phenomenon initi...

Hello-goodbye effect : The phenomenon initially described in psychotherapy research, but one which might arise whenever a subject is assessed on two occasions, with some interventi

Higher criticism, Higher criticism is a multiple-comparison test concept a...

Higher criticism is a multiple-comparison test concept arising from the situation where there are number of independent tests of significance and interest lies in the rejecting jo

Contour plot, Contour plot : A topographical map drawn from data comprising...

Contour plot : A topographical map drawn from data comprising observations on the three variables. One variable is represented on horizontal axis and the second variable is represe

Write Your Message!

Captcha
Free Assignment Quote

Assured A++ Grade

Get guaranteed satisfaction & time on delivery in every assignment order you paid with us! We ensure premium quality solution document along with free turntin report!

All rights reserved! Copyrights ©2019-2020 ExpertsMind IT Educational Pvt Ltd