השוואת שיטות

	K-means מאוחד (Ensemble K-means)	אשכול K-means
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2002	1967 (formalized 1982)
הוגה השיטה	Strehl, A. & Ghosh, J.	MacQueen, J. B.; Lloyd, S. P.
סוג	Ensemble clustering (consensus aggregation of K-means partitions)	Partitional clustering
מקור מכונן	Strehl, A. & Ghosh, J. (2002). Cluster ensembles — a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3, 583–617.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
כינויים	consensus K-means, K-means ensemble clustering, cluster ensemble with K-means, EKM	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
קשורות	3	4
תקציר	Ensemble K-means runs K-means clustering many times under varied initializations, random seeds, or feature subsets, then aggregates the resulting partitions into a single consensus assignment. This approach reduces K-means' well-known sensitivity to initialization and produces more stable, reproducible clusters than any single run.	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.