Automatic dating of documents and temporal text classification
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The algorithm performs in O(n log n) time for input of length n.
Using a different distance function other than (squared) Euclidean distance may stop the algorithm from converging.
The problem is computationally difficult (NP-hard); however, efficient heuristic algorithms converge quickly to a local optimum.
These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both k-means and Gaussian mixture modeling.
The Forgy method tends to spread the initial means out, while Random Partition places all of them close to the center of the data set.
According to Hamerly et al., the Random Partition method is generally preferable for algorithms such as the k-harmonic means and fuzzy k-means.
), where each observation is a d-dimensional real vector, k-means clustering aims to partition the n observations into k (≤ n) sets S = so as to minimize the within-cluster sum of squares (WCSS) (i.e. Formally, the objective is to find: The most common algorithm uses an iterative refinement technique.