Hierarchical Grouping to Optimize an Objective Function

By J. H. Ward. 1963

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Citations in the corpus

Interpretability Beyond Classification Output: Semantic Bottleneck Networks (2019, methodology): “In this paper, we approach the global error mode inspection via utilizing agglomerative clustering [11], enabling the investigation of errors on the cluster level and sample level as well contrasting errors with the closest true positive cluster (see figure 3 for a schematic of the method).”
PotentialNet for Molecular Property Prediction (2018, methodology): “Agglom-erative clustering was performed with Ward’s method 58 .”
Community detection in graphs (2009, methodology): “Vertices are then grouped into communities through an agglomerative hierarchical clustering technique based onWard’s method [265].”
CARER: Contextualized Affect Representations for Emotion Recognition (2018, methodology): “We use Ward’s method (Ward Jr, 1963) as the linkage criterion and cosine distance as the distance metric.”
Encoding of phonology in a recurrent neural model of grounded speech (2017, methodology): “We then performed agglomerative hierarchical clustering on phoneme type MFCC and activation vectors, using Euclidean distance as the distance metric and the Ward linkage criterion (Ward Jr, 1963).”
Genetic diversity for drought resistance in wild emmer wheat and its ecogeographical associations (2005, methodology): “An agglomerative hierarchical procedure with an incremental sum of squares grouping strategy, known as Ward’s method (Ward 1963), was employed for the purpose of classification.”
Comparing community structure identification (2005, methodology): “y the intuitive idea that a random walker will get trapped for a longer time in a a densely connected community. They calculate a distance measure between two nodes, and apply an agglomerative method [53], starting with all nodes in their own community, and joining them two by two. The main diﬀerenc”
Understanding trajectories of population decline across rural and urban Europe: A sequence analysis (2022, methodology): “The Ward’s hierarchical ascending clustering algorithm (Ward 1963) was used.”
Measuring Scholarly Impact (2014, methodology): “We now live in the era of Big Data and fortunately we have several tools for classifying data from many different sources, including point clouds, images, text documents (Blei, Ng, & Jordan, 2003; Kanungo et al., 2002; Ward, 1963), and networks.”
Genetic associations, variability and diversity in seed characters, growth, reproductive phenology and yield in Jatropha curcas (L.) accessions (2008, methodology): “Hierarchical clustering by Ward’s minimum variance cluster analysis method ( Ward 1963 ) for both seed characters and growth data for all the accessions revealed the genetic relatedness betweenaccessions (Fig. 4). More truncations were seen in both seed characters and growth with two members in a cl”
A Survey of Clustering Data Mining Techniques (2006, methodology): “One of early agglomerative clustering algorithms, Ward’s method [222], is based not on linkage metric, but on an objective function used in k-means.”