In this post, I will discuss the calculation of K-Nearest Neighbor with Minkowsky Distance.
The classification that will be discussed is the Classification of Scholarship Recipients.
The data we will use is:
- Consists of 10 data
- Consists of 2 criteria
- K = 5
- r Minkowsky = 4
Information :
- Which will be categorized consists of Non-Academic values and Academic values.
- Status consists of Not Qualified and Qualified.
- What we will classify is Name = See, Non-Academic Value = 81 and Academic Value = 86, Status = ?
Calculation (formula to be used) :
- 79 is Alan's Non-Academic score
- 81 is See's Non-Academic score
- 97 is Alan's Academic score
- 86 is See's Academic score
- 1/4 is a power value, because r = 4
- Then do the same calculations on data belonging to Dani, Rey and so on.
Then the calculation will be obtained as follows :
After that, do the ranking from the smallest to the largest value, the results obtained are as follows :
After getting the ranking, we can ensure that on behalf of See is eligible to receive a Scholarship or Not Eligible to receive a Scholarship.
Explanation :
- If using K=1 then the result is Not Qualified because Moon the result is Not Qualified.
- If using K=3 then the result is Not Qualified because Dani's result is Qualified, while Moon and Tet the result is Not Qualified (Qualified is 1 and Unqualified is 2).
- If using K=5 then the result is Not Qualified because Dani's result is Qualified, while Moon, Tet, Cee and Jo the result is Not Qualified (Qualified is 1 and Unqualified is 4).
So that the final result of See Not Qualifying
Notes :
- The value of K is independent (try to have an odd number so it doesn't confuse us when there are the same number of voting results).
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