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References in periodicals archive ?
Thus, the KSI corresponds to a 'squared Euclidean distance', which is a special form of power distance that is discussed in applied mathematics (Deza and Deza 2016).
(2010), a dendrogram was constructed based on squared Euclidean distance matrix data sets by applying un-weighted pair group method of arithmetic average (UPGMA) cluster analysis using Minitab ver.17 software.
Our key idea is to compute the fraction value between the two squared Euclidean distances, based on which AW is able to judge its relationship and returns EW as an encrypted identifier that indicates the minimum value.
For example, Euclidean distances or Squared Euclidean Distances are used more often in order to assign progressively greater weight on objects that are further apart (Mooi, Sarstedt, 2011).
We choose the squared Euclidean distance from [H.sub.kk][W.sub.k] to the interference subspace [[].sub.k] of receiver k as the measurement of the extent by which the desired signal is away from the interference signal subspace at receiver k.
The mixed model takes its point of departure in the formula for the squared Euclidean distance between a voter and a party, that is, [[absolute value of i].sup.2] + [[[absolute value of j].sup.2] 2i [multiplied by j], where the first two terms represent the squared lengths of the voter and party vectors and the third twice the
where [d.sup.2](P,Q) is the squared Euclidean distance between the centroids of P and Q.
Squared euclidean distance was used as the measure of pairwise profile similarity in this research.
Thirty insect-resistant transgenic upland cotton crosses were divided into three types (high, medium, low) based on lint yield per hectare by squared Euclidean distance and Ward linkage.
In this study, clustering was based on Gower's distance between individuals and the Squared Euclidean distance between groups.
To use a minimum distance classifier, the squared Euclidean distance between the object to be classified and each trained class center is to be find by summing up all the block distances between the object blocks and the trained block means in the eigenblock space.