Compară metode

	Clusteringul K-Means Regularizat	Clustering K-means
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2010	1967 (formalized 1982)
Autorul original	Witten, D. M. & Tibshirani, R. (sparse k-means formulation)	MacQueen, J. B.; Lloyd, S. P.
Tip	Regularized unsupervised clustering	Partitional clustering
Sursa seminală	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.
Denumiri alternative	sparse k-means, penalized k-means, regularized clustering, constrained k-means	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Înrudite	2	4
Rezumat	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.