This system employs K-means in cluster analysis
and divides the datasets into different groups depending on certain parameters chosen from the outcome of the ontology and categorizing steps.
An initial two-step cluster analysis
was performed entering all the technical test parameters (amplitude and frequency domains) just defined.
was used to group genotypes by Ward method.
The retrospective part of the study serves as a proof of principle and clearly demonstrates increased resolution of whole-genome cluster analysis
for typing of common PFGE pattern types and subsequent outbreak detection.
Table 5 details the results of cluster analysis
for each port group separately.
Spatial cluster analysis
is not only an important part of spatial data mining, but also widely used in spatial data mining and in-depth study of one of the elements.
Beyers notes that he is unaware of others using cluster analysis
to study state industry structure and unemployment trends and asserts that cluster analysis
could be useful in future research into the relationship between industry structure and unemployment.
The segmentation of customers is a standard application of cluster analysis
which allows segments to be formed that are based on data that are less dependent on subjectivity (Mooi & Sarstedt, 2011).
Most commonly two groups of cluster analysis
methods are distinguished: a hierarchical cluster analysis
and non-hierarchical cluster analysis
It was observed from the cluster analysis
in the present study that distinct new clusters are created with most impact on the rainfall data.
is a multivariate procedure that simultaneously considers all classification variables to arrange a sample of entities into distinct groups according to shared characteristics (Stefos, Lavallee, and Holden 1992).
The researchers determined beverage consumption patterns among Canadian children aged two years using cluster analysis
where sociodemographics, ethnicity, household income, and food security were significantly different across the clusters.