Difference between revisions of "Hierarchical Cluster Analysis (HCA)"
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# Compute distance between clusters (items) | # Compute distance between clusters (items) | ||
# Return to step 1 | # Return to step 1 | ||
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| + | The HCPC (Hierarchical Clustering on Principal Components) approach allows us to combine the three standard methods used in multivariate data analyses (Husson, Josse, and J. 2010): | ||
http://www.sthda.com/english/sthda-upload/figures/principal-component-methods/011-hcpc-hierarchical-clustering-on-principal-components-3d-map-1.png | http://www.sthda.com/english/sthda-upload/figures/principal-component-methods/011-hcpc-hierarchical-clustering-on-principal-components-3d-map-1.png | ||
Revision as of 19:13, 22 April 2019
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- Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)
- Hierarchical Temporal Memory (HTM)
- Capabilities
- ...find outliers
- Identify clusters (items) with closest distance
- Join them to new clusters
- Compute distance between clusters (items)
- Return to step 1
The HCPC (Hierarchical Clustering on Principal Components) approach allows us to combine the three standard methods used in multivariate data analyses (Husson, Josse, and J. 2010):
