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Consider the following dataset consisting of 1 0 instances in ( x , y ) coordinates, classified as either Class P or Class N: P

Consider the following dataset consisting of 10 instances in (x, y) coordinates,
classified as either Class P or Class N:
P1 : (2.3,3.1)- Class P
P2 : (5.0,6.0)- Class P
P3 : (6.0,3.0)- Class P
P4 : (4.0,4.0)- Class P
P5 : (6.0,5.0)- Class P
N1 : (7.0,7.0)- Class N
N2 : (8.8,3.2)- Class N
N3 : (9.6,6.1)- Class N
N4 : (6.2,8.2)- Class N
N5 : (4.5,7.9)- Class N
A new instance U of unknown class is at (6,6).
(a) Use the KNN-algorithm to determine the class of U.
i. First use the Euclidian distance measure (see Appendix on p.8) and k =5.
Show all your distance calculations, the ordering of instances, and the
process of selecting the class for the unknown instance U.
Appendix-log2
for some fractions
x log2
(x)
1/2=0.5001.000
1/3=0.3331.585
2/3=0.6670.585
1/4=0.2502.000
3/4=0.7500.415
1/5=0.2002.322
2/5=0.4001.322
3/5=0.6000.734
4/5=0.8000.322
1/6=0.1672.585
5/6=0.8330.263
1/7=0.1432.807
2/7=0.2861.807
3/7=0.4291.222
4/7=0.5710.807
5/7=0.7140.485
6/7=0.8570.222
2/9=0.2222.170
4/9=0.4441.170
5/9=0.5560.848
7/9=0.7780.363
8/9=0.8890.170
1/36=0.0285.170
5/36=0.1392.848
7/36=0.1942.363
11/36=0.3061.710
13/36=0.3611.469
17/36=0.4721.082
19/36=0.5280.922
23/36=0.6390.646
25/36=0.6940.526
29/36=0.8060.312
31/36=0.8610.216
35/36=0.9720.041
1=1.0000.000

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