Hierarchical Clustering Analysis
Description
- These are all non-hypothesis techniques.
- Clustering is achieved on the basis of a defined or measured 'distance' or 'similarity' between individuals / objects.
- Those objects with a high degree of 'similarity' will be placed in the same 'cluster'.
- Those items that appear 'distant' or dissimilar from the first group will be placed in another cluster.
- Eventually all individuals / objects will be allocated to a cluster. The effect is to produce a hierarchy of clusters and these can be represented by a dendrogram.
Usage
| Field | Description |
|---|---|
| Launch | Field indicating whether you want to execute this analysis in the workflow (checked) or not (unchecked). |
| Distance | Measure of dissimilarity between sets of observations |
| Agglomeration Method | Function of the pairwise distances between observations |
| Reverse HeatMap | By default, for the representation of the HeatMap, the samples are in the height direction, the variables in the direction of the width. You can reverse the Heat Map by checking this option at Yes |
Output Examples
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