手法を比較

	アンサンブルK-means	K-means クラスタリング
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	2002	1967 (formalized 1982)
提唱者	Strehl, A. & Ghosh, J.	MacQueen, J. B.; Lloyd, S. P.
種類	Ensemble clustering (consensus aggregation of K-means partitions)	Partitional clustering
原典	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.
別名	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
関連	3	4
概要	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.