Description
Implement (in C++) the K-means clustering algorithm [MacQueen, 1967] with a Euclidean distance metric. See online tutorials at:
- http://www.saedsayad.com/clustering kmeans.htm
Use the implemented K-means algorithm to cluster the following 8 examples (table 1) into 3 clusters.
When running K-means, set the initial seeds (initial centroid of each cluster) as examples 1, 4 and 7.
Table 1: Data (examples have two attributes: X, Y , both in range: [1, 10]).
| Example Number | X | Y |
| 1 | 2 | 10 |
| 2 | 2 | 5 |
| 3 | 8 | 4 |
| 4 | 5 | 8 |
| 5 | 7 | 5 |
| 6 | 6 | 4 |
| 7 | 1 | 2 |
| 8 | 4 | 9 |
Question 1: How many iterations are needed for k-means to converge?
In a text file output the results of each iteration (for each cluster, list the examples that fall into each cluster), and the centroids of each cluster, e.g.:
Iteration 1
Cluster 1: 1, 2, 3
Centroid: (3.0, 9.5)
Cluster 2: 4, 5, 6
Centroid: (6.5, 5.25)
Cluster 3: 7, 8
Centroid: (1.5, 3.5)
- ··
Iteration N
Cluster 1: 8, 7, 6
Centroid: (1.5, 3.5)
Cluster 2: 5, 4, 3
Centroid: (6.5, 5.25)
Cluster 3: 2, 1
Centroid: (3.0, 9.5)
In a ZIP file, place the source code, makefile, and the output text file (answer to question 1). Upload the ZIP file to Vula before 10.00 AM, Monday, 12th of August, 2019.
References
[MacQueen, 1967] MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Berkeley Symposium on Mathematics, Statistics and Probability, pages 281–297, Berkeley, USA. University of California Press.
