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Is K-Means clustering deterministic?
The basic k-means clustering is based on a non-deterministic algorithm. This means that running the algorithm several times on the same data, could give different results.
Why is K-Means clustering non-deterministic?
The non-deterministic nature of K-Means is due to its random selection of data points as initial centroids.
What is the difference between agglomerative and divisive clustering methods?
Agglomerative clustering makes decisions by considering the local patterns or neighbor points without initially taking into account the global distribution of data. whereas divisive clustering takes into consideration the global distribution of data when making top-level partitioning decisions.
Which is a step in the agglomerative clustering process?
Agglomerative Clustering is a member of the Hierarchical Clustering family which work by merging every single cluster with the process that is repeated until all the data have become one cluster. The step that Agglomerative Clustering take are:
Which is more deterministic DBSCAN or hierarchical agglomerative clustering?
Hierarchical Agglomerative Clustering is deterministic except for tied distances when not using single-linkage. DBSCAN is deterministic, except for permutation of the data set in rare cases. k-means is deterministic except for initialization.
When do you need a deterministic clustering method?
I need a deterministic [in the sense – robust to the ways of initial input / initial seeds] clustering method to group values in distributions that could be either random, normal or log-normal. Google mostly turns up k-means, which isn’t deterministic.
How to calculate the similarity between two clusters?
There are some methods which are used to calculate the similarity between two clusters: Distance between two closest points in two clusters. Distance between two farthest points in two clusters. The average distance between all points in the two clusters. Distance between centroids of two clusters.