Linganisha mbinu

	Ulinganifu wa Kati wa DTW	Ngeli ya Kiwango cha Juu (Hierarchical Clustering)
Nyanja	Mfululizo wa Muda	Ujifunzaji wa Mashine
Familia	Process / pipeline	Machine learning
Mwaka wa asili	2011	1963
Mwanzilishi	François Petitjean	Ward, J. H.
Aina	Distance-based time-series aggregation	Unsupervised clustering (agglomerative)
Chanzo asilia	Salvador, S., & Chan, P. (2004). FastDTW: Toward accurate dynamic time warping in linear time and space. Intelligent Data Analysis, 11(5), 561–580.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Majina mbadala	DBA, DTW-BA, Barycenter Averaging	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Zinazohusiana	4	4
Muhtasari	DTW Barycenter Averaging (DBA) is a method for computing the average or representative sequence of a set of time series that respects temporal warping and elastic distance. Unlike Euclidean averaging which requires point-wise alignment, DBA minimizes the sum of Dynamic Time Warping (DTW) distances, producing a meaningful average for sequences with flexible temporal alignments. Introduced by Petitjean and colleagues in 2011, it is widely used in time-series clustering and summarization.	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.