Assignment
1. Let the training set consist of patterns in the following table. Let the
test patten be P = (3.0, 2.0)t. Use the Branch and Bound NNC to
classify P using the training data shown in problem 1 in module 7. Use
each class as a cluster at the top level. Let lower level clusters be
Cluster 1a = {X1,X2,X3} Cluster 1b = {X4,X5}
Cluster 2a = {X7,X8,X6,X11} Cluster 2b = {X9,X10,X12}
Cluster 3a = {X13,X14,X15} Cluster 3b = {X16,X17,X18}
Figure 3: kd tree
2. Use the projection algorithm to classify P using relevant part of the
data given in problem 1.
3. Consider the following three-dimensional patterns. Using ordered par-
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