Módszerek összehasonlítása

	K-Means klaszterezés	Szabályindukció (RIPPER)
Tudományterület	Gépi tanulás	Gépi tanulás
Módszercsalád	Machine learning	Machine learning
Keletkezés éve	1967	1995
Megalkotó	MacQueen, J.	William W. Cohen
Típus	Partitional clustering (centroid-based)	Supervised rule learning algorithm
Alapmű	MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.	Cohen, W. W. (1995). Fast effective rule induction. Proceedings of the 12th International Conference on Machine Learning, 115–123.
Alternatív nevek	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering	RIPPER, Propositional Rule Learning, Kural Tümevarımı, Inductive Rule Learning
Kapcsolódó	3	2
Összefoglaló	K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.	Rule Induction, and specifically the RIPPER (Repeated Incremental Pruning to Produce Error Reduction) algorithm, is a supervised machine learning method that learns a compact set of IF-THEN classification rules from labeled training data. Introduced by William W. Cohen in 1995, RIPPER applies a separate-and-conquer strategy combined with minimum description length (MDL) pruning to generate rules that are both accurate and interpretable, making it a landmark algorithm in the field of inductive rule learning.