K-means
In previous exercise we were using etalons as classification, which were computed from a labeled set of objects in class. In this exercise we will try to compute the etalons values by a clustering algorithm K-means.
K-means is iteration algorithm, where in each iteration we specify a position of centroid, we will label all objects in our features domain, and compute a new center of mass for our centroids. This will then continue to next iteration. We stop the iteration process at the moment, where our change in position for our centroids is small, and therefore we converged our centroids position to solution.
As long as this algorithm is depended on initial conditions, its adviced to set a constant starting positions, and to figure our which centroid is labeled by a certain object.
K-means
TBD
TBD
| Data | Value |
|---|---|
| Source | https://cs.wikipedia.org/wiki/K-means |
| Code |