Authors:Sally M. Gainsbury, Daniel L. King, Alex M. T. Russell, and Paul Delfabbro
continuous, Mann–Whitney U tests or Spearman’s rho were used. A two-step clusteranalysis was performed to determine groups of people who pay for SCG based on reported frequency of sessions and expenditure per session. An alpha level of .05 was employed
Authors:Jeba Emilyn Jeyaswamidoss, Kesavan Thangaraj, Kadarkarai Ramar, and Muthusamy Chitra
addition to the range of clusteranalysis techniques available to researchers. The concept of rough sets has been introduced into clustering lately and a very few clustering algorithms have also been developed based on rough set theory [10–12] . A
Authors:George A. Barnett, Catherine Huh, Youngju Kim, and Han Woo Park
more centrally. Eigenvector centrality determines a journal's overall centrality in the network (Bonacich 1972 ).
Another indicator of the structure of the citation pattern is how journals cluster. Clusteranalysis identifies groups within data
, multivariate statistical methods, principal component analysis (PCA) and clusteranalysis (CA) [ 20 , 21 ]. By assessing of the influence of different substituents on the course of thermal decomposition of α-amino acids, the results can be useful for
genetic distances based on RAPD and SSR banding pattern had been used for clusteranalysis to find the genetic relationship between 24 genotypes of moss H. involuta in the form of dendrogram. The clusters generated by UPGMA method are represented in
Authors:Nicolas A. Bonfils, Marie Grall-Bronnec, Julie Caillon, Frédéric Limosin, Amine Benyamina, Henri-Jean Aubin, and Amandine Luquiens
classification analysis) and the major associated words (χ 2 ≥ 12) on the corpus [form (χ 2 /Φ/frequency)]. Note. Theses major associated words were translated from French to English
A clusteranalysis of this class
Authors:J. Švedkauskaitė-LeGore, G. Rasmussen, S. Abousahl, and P. van Belle
A set of 35 uranium ore and 10 yellow cake samples, collected worldwide from different mines and production sites, were analyzed
for their impurity spectrum by ICP-MS. Pattern recognition techniques such as cluster analysis were applied to the data set
in order to characterize samples with relation to their geographical origin. The results obtained show a clear relationship
between samples taken from the same geological origin and constitute a satisfactory fingerprint for establishing the origin
of the material. In addition to the impurity data, data on the isotopic composition of radiogenic lead is used to resolve
ambiguity when impurity cluster analysis fails to deliver unambiguous origin data.
This paper describes the implementation of multivariate data analysis: NEURODOC applies the axial k-means method for automatic,
non-hierarchical cluster analysis and a Principal Component Analysis (PCA) for representing the clusters on a map. We next
introduce Artificial Neural Networks (ANNs) to extend NEURODOC into a neural platform for the cluster analysis and cartography
of bibliographic data. The ANNs tested are: the Adaptive Resonance Theory (ART 1), a Multilayer Perceptron (MLP), and an associative
network with unsupervised learning (KOHONEN). This platform is intended for quantitative analysis of information.
Authors:A. Cesana, A. Ciasca, N. Cuomo Di Caprio, M. Terrani, and V. Tusa
The concentration of 7 elements (Na, Al, Mg, Ti, Ca, V, Mn) was determined by Neutron Activation Analysis in 35 samples of
pottery and 14 samples of clay. The samples were collected in Mothia (a Phoenician stronghold in Sicily during 5th–4th century B. C.) and in its neighbourhoods. Cluster analysis of the data showed that most of the samples are homogeneous and
confirmed the archaeological evidence that they are mostly local ware. The detailed results of the analyses are reported and
the technique used for cluster analysis is described.