Finally, this distance matrix is used as input for an agglomerative clustering algorithm with average weights, which generates the dendrograms in Fig.2.
Maximal-entropy random walk unifies centrality measures
Other hierarchical clustering approaches such as repeated bisecting k-means, K-tree and agglomerative hierarchical clustering have also been used.
Document Clustering Evaluation: Divergence from a Random Baseline
Our algorithm falls in the general category of agglomerative hierarchical clustering methods [15, 16].
Fast algorithm for detecting community structure in networks
With this ∆V, collisions in the belt are primarily erosive, not agglomerative, leading to the idea that the Kuiper belt is eroding away.
Icy Bodies in the New Solar System
Neighbor Joining is an agglomerative (bottom up) method which is computationally inexpensive and is thus often used as a starting point for other tree estimation procedures.
Computational Tools for Evaluating Phylogenetic and Hierarchical Clustering Trees
Hierarchical Clustering We apply hierarchical clustering in an agglomerative (bottomup) manner.
Product Review Summarization based on Facet Identification and Sentence Clustering
In agglomerative clustering methods, for example, this parameter deﬁnes the level of the resulting dendrogram at which the clustering solution is identiﬁed (Jain and Dubes, 1988).
Resampling Method For Unsupervised Estimation Of Cluster Validity
In particular, agglomerative algorithms, which provide a hierarchy of clusters, always generate some hierarchy as a control parameter is varied.
Resampling Method For Unsupervised Estimation Of Cluster Validity
The latter is an agglomerative hierarchical method.
Resampling Method For Unsupervised Estimation Of Cluster Validity
Euclidean classiﬁcation is most in harmony with Correspondence Analysis when minimum variance (or Ward’s) agglomerative hierarchical clustering is used.
Geometric Data Analysis, From Correspondence Analysis to Structured Data Analysis (book review)
Pride of place is accorded agglomerative hierarchical clustering with the minimum variance criterion (Ward’s method; “Euclidean classiﬁcation” since the input consists of Euclidean pro jections on factors).
Geometric Data Analysis, From Correspondence Analysis to Structured Data Analysis (book review)
The reciprocal nearest neighbors algorithm is described but not the nearest neighbor chain algorithm (which is more manageable from a computational complexity point of view, since it has an O(n2 ) computational bound for most widely used agglomerative criteria).
Geometric Data Analysis, From Correspondence Analysis to Structured Data Analysis (book review)
At every step, all possible mergers of two clusters are tried and the one with the smallest increase is selected. The agglomerative hierarchical clustering method often suffers from adjustment problem.
Interest Rate Manipulation Detection using Time Series Clustering Approach
In order to do so, we cluste r the set of signiﬁcant interests I′ using a hierarchical agglomerative clustering algorithm with a complete linkage strategy (Kantardzic, 2011; Everitt, 2001).
Inference of the Russian drug community from one of the largest social networks in the Russian Federation
***