Difference between revisions of "Hierarchical Cluster Analysis (HCA)"
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* [[Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)]] | * [[Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)]] | ||
* [[Hierarchical Temporal Memory (HTM)]] | * [[Hierarchical Temporal Memory (HTM)]] | ||
| − | * [[... | + | * [[Embedding]]: [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]] ... [[...find outliers]] |
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# Identify clusters (items) with closest distance | # Identify clusters (items) with closest distance | ||
Revision as of 00:41, 11 July 2023
YouTube search... ...Google search
- Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)
- Hierarchical Temporal Memory (HTM)
- Embedding: Search ... Clustering ... Recommendation ... Anomaly Detection ... Classification ... Dimensional Reduction ... ...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):
