Page 32 - Demo

%u062c%u0645%u064a%u0639 %u0627%u0644%u062d%u0642%u0648%u0642 %u0645%u062d%u0641%u0648%u0638%u0629 %u0640 %u0627%u0625%u0644%u0639%u062a%u062f%u0627%u0621 %u0639%u0649%u0644 %u062d%u0642 %u0627%u0645%u0644%u0624%u0644%u0641 %u0628%u0627%u0644%u0646%u0633%u062e %u0623%u0648 %u0627%u0644%u0637%u0628%u0627%u0639%u0629 %u064a%u0639%u0631%u0636 %u0641%u0627%u0639%u0644%u0647 %u0644%u0644%u0645%u0633%u0627%u0626%u0644%u0629 %u0627%u0644%u0642%u0627%u0646%u0648%u0646%u064a%u062932xn1Dissimilarity matrix (or object-by-object structure): This structure stores a collection of proximities that are available for all pairs of n objects. It is often represented by an n-by-n table: 0d(2,1) d(3,1):d(n,1)0 d(3,2):d(n,2)0:... ... 01-Proximity Measure for Nominal Attributes: A nominal attribute can take on two or more states For example,mapcolor is a nominal attribute that may have, five states: red, yellow, green, pink and blue. Compute it by: d(i, j)= p%u2212pmwhere m is the number of matches and p is the total number of attributes describing the objects. For examaples visit : https://www.youtube.com/watch?v=Yj4wfLgHbf02- Proximity Measure for Binary Attributes A contingency table for binary data