clusterings

Does K-means Always Converge To The Same?

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Does K-means Always Converge To The Same? 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

Does K Mean Soft Clustering?

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Does K Mean Soft Clustering? Does K mean soft clustering? Fuzzy clustering (also referred to as soft clustering or soft k-means) is a form of clustering in which each data point can belong to more than one cluster. What does K mean clustering mean? K-means clustering is a simple unsupervised learning algorithm that is used