Use K-Means Algorithm to create two clusters- Solution- We follow the above discussed K-Means Clustering Algorithm. For the K-means algorithm the distance is always Euclidean distance and the new center is the component-wise mean of the data in the cluster. The following formula is used to calculate the euclidean distance between points.
For the K-means algorithm the distance is always Euclidean distance and the new center is the component-wise mean of the data in the cluster.
It picks up the range of values and takes the best among them. And a slow k-means will mean that you have to wait longer to test and debug your solution. Lets calculate cluster for only one feature X 12 67810151720 and assume cluster K 3. A euclidean distance is defined as any length or distance found within the euclidean 2 or 3 dimensional space.