q-mode hierarchical cluster (hca) and principal component analysis (pca) were simultaneously applied to groundwater hydrochemical data from the three times in 2004: june, september, and december, along the ain azel aquifer, algeria, to extract principal factors corresponding to the different sources of variation in the hydrochemistry, with the objective of defining the main controls on the h...

Lensing by galaxy clusters is a versatile probe of cosmology and extragalactic astrophysics, but the accuracy some its predictions limited simplified models adopted to reduce (otherwise untractable) number degrees freedom. We aim at cluster lensing where parameters all cluster-member galaxies are free vary around common scaling relations with non-zero scatter, deviate significantly from them if...

Clustering ensemble performance is affected by two main factors: diversity and quality. Selection of a subset of available ensemble members based on diversity and quality often leads to a more accurate ensemble solution. However, there is not a certain relationship between diversity and quality in selection of subset of ensemble members. This paper proposes the Hierarchical Cluster Ensemble Sel...

Graphs have so many applications in real world problems. When we deal with huge volume of data, analyzing data is difficult or sometimes impossible. In big data problems, clustering data is a useful tool for data analysis. Singular value decomposition(SVD) is one of the best algorithms for clustering graph but we do not have any choice to select the number of clusters and the number of members ...

Background. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Provided Data Set. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Exercise. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...

This paper introduces a hybrid hierarchical clustering method, which is a novel method for speeding up agglomerative hierarchical clustering by seeding the algorithm with clusters obtained from K-means clustering. This work describes a benchmark study comparing the performance of hybrid hierarchical clustering to that of conventional hierarchical clustering. The two clustering methods are compa...