Linganisha mbinu

	K-Means Clustering	Uundaji wa Kanuni (RIPPER)
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1967	1995
Mwanzilishi	MacQueen, J.	William W. Cohen
Aina	Partitional clustering (centroid-based)	Supervised rule learning algorithm
Chanzo asilia	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.
Majina mbadala	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering	RIPPER, Propositional Rule Learning, Kural Tümevarımı, Inductive Rule Learning
Zinazohusiana	3	2
Muhtasari	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.