1 Answer. The algorithm always converges (by-definition) but not necessarily to global optimum. The algorithm may switch from centroid to centroid but this is a parameter of the algorithm ( precision , or delta ). This is sometimes refered as “cycling”.
Why k-means not converge?
explained, the K-means algorithm depends on the initial cluster centroid positions, and there is no guarantee that it will converge to the optimal solution . The best you can do is to repeat the experiment several times with random starting points.
Why does K-means always converge?
Since the algorithm iterates a function whose domain is a finite set, the iteration must eventually enter a cycle. Hence k-means converges in a finite number of iterations .
Does k-means always converge to the global minima why why not?
There are (at least) two different settings which satisfy the stationary condition of k-means. here the objective is 1/4. If k-means would be initialized as the first setting then it would be stuck.. and that’s by no means a global minimum .
Does k-means always terminate?
Theoretically, k-means should terminate when no more pixels are changing classes . There are proofs of termination for k-means. These rely on the fact that both steps of k-means (assign pixels to nearest centers, move centers to cluster centroids) reduce variance.
How do you stop K-means?
- Centroids of newly formed clusters do not change.
- Points remain in the same cluster.
- Maximum number of iterations are reached.
What are the drawbacks of K-means algorithm?
It requires to specify the number of clusters (k) in advance. It can not handle noisy data and outliers. It is not suitable to identify clusters with non-convex shapes .
Why choose K-Means clustering?
The K-means clustering algorithm is used to find groups which have not been explicitly labeled in the data . This can be used to confirm business assumptions about what types of groups exist or to identify unknown groups in complex data sets.
Why is k-means bad?
K-Means clustering algorithm fails to give good results when the data contains outliers, the density spread of data points across the data space is different and the data points follow non-convex shapes.
How does K mean?
K-means clustering uses “centroids”, K different randomly-initiated points in the data, and assigns every data point to the nearest centroid. After every point has been assigned, the centroid is moved to the average of all of the points assigned to it.
Will K-means always achieve the optimal clustering?
The algorithm does not guarantee convergence to the global optimum. The result may depend on the initial clusters. As the algorithm is usually fast, it is common to run it multiple times with different starting conditions.
Does K mean supervised learning?
K-Means clustering is an unsupervised learning algorithm . There is no labeled data for this clustering, unlike in supervised learning. K-Means performs the division of objects into clusters that share similarities and are dissimilar to the objects belonging to another cluster. ... For example, K = 2 refers to two clusters.
What is K-means algorithm with example?
K-means clustering algorithm computes the centroids and iterates until we it finds optimal centroid. ... In this algorithm, the data points are assigned to a cluster in such a manner that the sum of the squared distance between the data points and centroid would be minimum.
Can K-means oscillate?
Andrew Ng’s lecture notes here has the statement “ it is possible for k-means to oscillate between a few different clusterings — i.e., a few different values for c and/or μ—that have exactly the same value of J, but this almost never happens in practice.)”
Is K-means supervised or unsupervised?
K-means is a clustering algorithm that tries to partition a set of points into K sets (clusters) such that the points in each cluster tend to be near each other. It is unsupervised because the points have no external classification.
What is the K in the K-means algorithm used for?
In other words, the K-means algorithm identifies k number of centroids , and then allocates every data point to the nearest cluster, while keeping the centroids as small as possible. The ‘means’ in the K-means refers to averaging of the data; that is, finding the centroid.
