Can we get different results for different runs of K-means clustering A?

Can we get different results for different runs of K-means clustering A?

K-Means clustering algorithm instead converses on local minima which might also correspond to the global minima in some cases but not always. However, note that it’s possible to receive same clustering results from K-means by setting the same seed value for each run.

Why applying K-means clustering to the same dataset twice may give different results?

K-means generally needs some initial cluster assignment or set of cluster centers to start with. The two differing results might hence likely be two local minima of the function (minimal distances to class means) that k-means optimizes.

Why do we need to run K-Means clustering algorithm multiple times to get the best solution?

Because the centroid positions are initially chosen at random, k-means can return significantly different results on successive runs. To solve this problem, run k-means multiple times and choose the result with the best quality metrics.

Is Kmeans clustering random?

K-Means clustering. The number of clusters to form as well as the number of centroids to generate. ‘random’: choose n_clusters observations (rows) at random from data for the initial centroids. …

How can you improve performance of K-means clustering?

K-means clustering algorithm can be significantly improved by using a better initialization technique, and by repeating (re-starting) the algorithm. When the data has overlapping clusters, k-means can improve the results of the initialization technique.

When to compare different methods for clustering data?

Going this way may mislead one to ignore disadvantages proposed method. For clustering results, usually people compare different methods over a set of datasets which readers can see the clusters with their own eyes, and get the differences between different methods results.

What kind of metrics are used for clustering?

For clustering results, usually people compare different methods over a set of datasets which readers can see the clusters with their own eyes, and get the differences between different methods results. There are some metrics, like Homogeneity, Completeness, Adjusted Rand Index, Adjusted Mutual Information, and V-Measure.

Can a clustering be consistent with a gold standard?

(See e.g. my answers here: Comparing clusterings: Rand Index vs Variation of Information and here: Forgiving measure for external cluster validation ). This is still not straightforward — a clustering can be consistent with a a gold standard (be a sub-clustering or super-clustering) and this has to be taken into account but often is not.

When to use the silhouette measure for clustering?

Because of this comparison, the silhouette measure is suitable for comparing clustering results that contain different numbers of clusters. If there are too many or too few clusters, the silhouette measure will be closer to zero than if an appropriate number of clusters is chosen.