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	DTW बैरीसेंटर एवरेजिंग	पदानुक्रमिक समूहन
क्षेत्र	समय श्रृंखला	मशीन अधिगम
परिवार	Process / pipeline	Machine learning
उद्भव वर्ष	2011	1963
प्रवर्तक	François Petitjean	Ward, J. H.
प्रकार	Distance-based time-series aggregation	Unsupervised clustering (agglomerative)
मौलिक स्रोत	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.
उपनाम	DBA, DTW-BA, Barycenter Averaging	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
संबंधित	4	4
सारांश	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.