I did some study on the k-means clustering algorithm. It seems that the only non-deterministic part is the centroid - initialization.
Assume I have 10k data points, and a given k. I then initialize the initial centroids randomly in my each try:
Try_1: Initial k-centroids randomly with seed_1. Then keep updating the centroids until converge (assuming we can use the 10k data points multiple times)
Try_2: Initial k-centroids randomly with seed_2. Then keep updating the centroids until converge (assuming we can use the 10k data points multiple times)
Try_3: Initial k-centroids randomly with seed_3. Then keep updating the centroids until converge (assuming we can use the 10k data points multiple times)
Try_4: Initial k-centroids randomly with seed_4. Then keep updating the centroids until converge (assuming we can use the 10k data points multiple times)
Try_5: Initial k-centroids randomly with seed_5. Then keep updating the centroids until converge (assuming we can use the 10k data points multiple times)
In these 5 tries, will the final cluster results be the same?