MULTIVARIATE analysisGENOTYPESCROP improvementK-means clusteringSTRAWBERRIESSTATISTICAL correlationELBOWMethods of multivariate analysis is a powerful approach to assist the initial stages of crops genetic impr
Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types through unsupervised clustering followed by manual annotation or via ‘mapping’ to existing data. However, manual interpretation scales...
E., (1996), An Empirical Comparison of Variable Standardization Methods in Cluster Analysis, Multivariate Behavioral Research, 31 (2), 149-167.C.M. Schaffer, and P.E. Green, "An empirical comparison of variable standardization methods in cluster analysis," Multivariate Behavioral Research, vol. ...
Clustering methodsare used to identify groups of similar objects in a multivariate data sets collected from fields such as marketing, bio-medical and geo-spatial. They are differenttypes of clusteringmethods, including: Partitioning methods Hierarchical clustering Fuzzy clustering Density-based clustering M...
In this process, we divided the basin into several clusters, by using k-means clustering technique. These clusters were not necessarily distinguished by their geographical extent and boundaries because we used a combination of geologic and geographical variables in our clustering model. We examine the...
2.2.3. Clustering local representatives In Step 3 of the bootstrap phase of the bagging clustering algorithm, the local representatives of the cells are clustered. This can be done in many different ways. Here we present the multivariate K-means method as well as one of its modifications that...
designing good clustering algorithm at the first place! –K-means can (and should) anyway be applied as fine- tuning of the result of another method. Two ways to improve k-means References 1. Forgy, E. W. (1965) Cluster analysis of multivariate data: efficiency vs interpretability ...
MacQueen J et al (1967) Some methods for classification and analysis of multivariate observations. Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Oakland, CA, USA 1:281–297 MathSciNet MATH Google Scholar Mandaglio D, Amelio A, Tagarelli A (2018) Consen...
Divisive clustering using Duda and Hart''s criterion performed satisfactorily and, encouragingly, gave similar results to the multivariate similarity measures when used on our data. However, the relative performance of the k-means techniques is likely data dependent, so one method is not likely to ...
Smile-GAN is a Generative Adversarial Network architecture for clustering a group based on their multivariate differences to a reference group. The idea of the model is realized by learning one mapp…