Salīdzināt metodes

	K-Means klasterizācijas regularizācija	K-means klasterizācija
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2010	1967 (formalized 1982)
Autors	Witten, D. M. & Tibshirani, R. (sparse k-means formulation)	MacQueen, J. B.; Lloyd, S. P.
Tips	Regularized unsupervised clustering	Partitional clustering
Pirmavots	Witten, D. M., & Tibshirani, R. (2010). A framework for feature selection in clustering. Journal of the American Statistical Association, 105(490), 713–726.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Citi nosaukumi	sparse k-means, penalized k-means, regularized clustering, constrained k-means	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Saistītās	2	4
Kopsavilkums	Regularized k-means extends standard k-means by adding a penalty term — most commonly an L1 (lasso-type) or L2 constraint — to the objective function. This discourages degenerate cluster solutions and, in the sparse variant introduced by Witten and Tibshirani (2010), simultaneously selects the features that drive cluster separation, making it especially valuable in high-dimensional settings where many features are irrelevant.	K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.