java code

group the received points, which reads 2-dimensional coordinates from a csv file, with k-means clustering. Here, the number of iterations and the number of clusters (K) are taken from the user. Coordinates are read by selecting a csv file with select from file button. When the K-Means Clustering Button is pressed, classification is made and the color is graded according to the class number. Here, the value of K can be selected from 1 to 10. When the K-Means Clustering Button is re-pressed, the new values are sorted and color-coded based on the new values.

You are asked to create a screen that makes this classification.

1. You can define the colors as standard. You can define fixed colors based on the value of k and adjust the color accordingly. You can set the dimensions of the points to a standard size that will be shown on the screen.

2. These more points in the blue color show the Cluster Centers. You can make a color yourself.

3. You can create your gore random centers for screen size.

4. A sample csv file will be provided with the tutorial.

5. When a file other than CSV file is selected, it should fail and not be processed.

6. When the K-Means Clustering button is pressed when the inter- ference and K value are re-entered, new values must be calculated.

K-Means Clustering: Determine Random Center as the K value entered. Each point to be classified is included in that group if it is closer to this K center. In each iteration, the new center of the Group is calculated and the Centers are updated. Gore re-grouping is performed on new centers. When the group centers as far as the intervention is guided, coloring is done according to the grouping in the last case.