Difference between revisions of "Hierarchical Cluster Analysis (HCA)"
m |
m |
||
| Line 19: | Line 19: | ||
* [[Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)]] | * [[Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)]] | ||
* [[Hierarchical Temporal Memory (HTM)]] | * [[Hierarchical Temporal Memory (HTM)]] | ||
| − | * [[Embedding]] | + | * [[Embedding]] ... [[Fine-tuning]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]]. [[...find outliers]] |
# Identify clusters (items) with closest distance | # Identify clusters (items) with closest distance | ||
Revision as of 17:39, 16 August 2023
YouTube search... ...Google search
- Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)
- Hierarchical Temporal Memory (HTM)
- Embedding ... Fine-tuning ... 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):
