Implementation of K-Nearest-Neighbour Algorithm using K-D Tree and custom build Priority Queue. From a given dataset, it gives us the nearest neighbour and K number of neighbours to any given point.
To run the program, compile and execute the main.c file
The input is taken from input.txt file
1st line: Dimensions - d
2nd line: Number of neighbours to find - K
3rd line: Number of input points of d dimension - number_points
4th line: the data point for which the neighbours are to be found
Then the data points are input line by line number_point times
For the given input file, the dataset repesent the following-
The columns are equal to the number of dimensions of the data points
The program will give the K nearest neighbours for the given point.
For the given input file:-
Given the number of dimensions - 4
Number of nearest neighbours to find- 10
Number of data points given - 10000
The data point around which neighbours to find - {689-7890-55-909}
Output on terminal:-
Result of Application OF Nearest Algorithm:
1 Nearest Point is {1446-5886-2679-699} at a Distance: 3393.897317
Results of Application OF KNN Algorithm :
1 Nearest Point is {1446-5886-2679-699} at a Distance: 3393.897317
2 Nearest Point is {2870-7522-474-3499} at a Distance: 3431.595256
3 Nearest Point is {2138-10700-530-2461} at a Distance: 3553.874927
4 Nearest Point is {4343-9578-1187-113} at a Distance: 4256.301094
5 Nearest Point is {4543-9680-110-1618} at a Distance: 4308.494169
6 Nearest Point is {3967-7037-2663-43} at a Distance: 4361.709757
7 Nearest Point is {1005-4940-2989-2625} at a Distance: 4511.692144
8 Nearest Point is {5133-8413-2210-994} at a Distance: 4967.284369
9 Nearest Point is {3322-13287-334-703} at a Distance: 6015.028928
10 Nearest Point is {4872-8106-3656-3343} at a Distance: 6036.199218